Hardware Encoding 4K HDR10 Videos?

We are about to venture into a heated and abnormal world. Hardware encoders, designed for real-time encoding, may be reaching the point they can also be considered for video archival. The three common consumers options available that we will look at are:

  • AMD’s VCN encoder (using 6900XT / RDNA2 architecture)
  • Nvidia’s NVENC encoder (using 3060 RTX / 7th generation)
  • Intel QSV encoder (using i7-11800H / Version 8)

These tests will all use HEVC UHD HDR10 source material and have valid UHD HDR10 10-bit output as well. This is not a common use case. These tests have been done because these encoders are being added to FastFlix. FastFlix is a free and open source GUI for common video encoding software, specifically designed to help with HDR10 videos.

What’s so odd about using a hardware encoder for encoding videos?

When looking to compress an existing video file, one of the main purposes is to save disk space, i.e. lower the bitrate. This can help save on disk space or on bandwidth usage if the file will be transferred a lot. For example, a single megabyte difference for a popular file on a large site could start costing hundreds of dollars of bandwidth fees.

That means you want to get as much quality as you can, into the smallest package possible. Whereas, historically, hardware encoders were designed with the singular purpose to encode above real-time speeds. That way they could be used with video conferencing like Zoom or transcode videos as needed as you watch them. They always wanted good quality of course, but speed was always more important.

However, as their hardware and software matured it is now reaching a point where they can reasonably be considered instead of using crazy slow software encoders. I believe a large part of this is due to the introduction of the B-frame.

The almighty B-frame

In HEVC videos there are three types of frames, Index (I) frames, Predicted (P) frames, and Bidirectionally Predicted (B) frames. I frames are pretty easy to understand, an I frame is a full picture. It’s what everyone thinks about when they assume a video file is a bunch of pictures in a row, like it was back with real film.

However with modern video codecs, like HEVC, there are in-between frames that don’t have the full picture, instead are half filled with a bunch of math (motion vectors) that say “hey, move that area over this way for this frame.” P-frames do just that, they use the data in the frame before them and store the different as motion vectors.


In idea scenarios, P-frames are about half the size of I-frames. That means if you have one I-frame and two P-frames after it for the entire movie, you’ve just cut off a third of the bitrate!

Linear-P-Frames Example


B-frames are even more efficient, they can be half the size of a P-frame in an ideal world, aka a quarter of an I frame. Imagine having a single I frame, then two B-frames, a P-frame, then two more B-frames. You would have knocked off almost 60% of the bitrate! However, the problem there is that B-frames are crazy hard to calculate. B is for Bidirectional, which means they not only look at the frame that was encoded before them, but also the frame that will come after them. That’s right, you have to first encode the I frame, and the P-frame (or another I frame) that comes after it, before calculating the B-frames between them.

B-frame non-linear encoding workflow

Until recently, hardware encoders were only thinking of moving in a forward direction. They never thought to stop to wait to build frames that came behind the current one, who would want that? Well without the B-frame, you have to either compensate by having more index frames or having larger P-frames. Take the following scenario.

B-Frames example

If the B-Frame was replaced with a P-frame (so it was I P P I) the P-frame with the sun in it would require additional image data stored in that frame. Whereas by using a B-frame, it can be stored as a motion vector, thus saving large amounts of bitrate.

Thankfully Nvidia and Intel have both decided that it’s time to bring some quality to the hardware world, and do have B-frames in their latest hardware encoders. Sadly, AMD still doesn’t have any support for it with HEVC videos.

Hardware Encoding Head to Head

This is probably why you’re here, to see how they stack up to each other. We are going to compare two videos against four different encoders. All encodes produced valid HDR10 videos and used the same settings per encode (except for one hiccup with Intel QSV erroring using –extbrc on one video.)


Tests like this are only as good as their documentation of how they were acquired. To that end, I wrote a script to run all these tests so that there were no quarrels about how they were tested (downloadable here). All tests were run on Windows 10 Version 10.0.19042 Build 19042 with the following settings.

The NVENC and QSV encodings were done on a laptop while plugged in on maximum power mode. The AMD VCN and x265 encodes were done on a desktop PC. Because of obvious hardware differences, encoding speed and power will not be considered as part of these tests.

AMD VCE / VCNNvidia NVENC Intel QSVx265 (Software)

(bold are
–ref 3
–preset slow
–tier High
–bframes 3
–ref 3
–preset quality
–tier high
–lookahead 16
–aq-strength 0
–multipass 2pass-full
–mv-precision Q-pel
–quality best
–la-depth 16
–la-quality slow

( –extbrc not set on
Glass Blowing)
-preset slow


Hardware6900 XT3060 RTXi7-11800Hi9-9900K
Driver21.7.2471.68 Game Ready27.20.100.974910.0.19041.546 (intelppl.sys)
SoftwareVCEEnc (x64) 6.13
AMF Version 1.4.21
NVEncC (x64) 5.37
CUDA 10.1
QSVEncC (x64) 5.06
Hardware API v1.35
FFmpeg ~4.4
x265 @ commit 82786fc

The x265 software was also given the benefit of running in dual pass mode. The slow preset was used as it was determined to be an ideal choice in previous tests.

The hardware encoders were set to use the “best” settings for measured quality, not perceived quality. I did not have as much time to test NVENC and QSV as I did VCN, so there may be more to eek out of those two encoders.


I wrote a python script to do all this testing, which can be downloaded: compare.py

If you want to check out the results of the VMAF / PSNR / SSIM yourself, here are the json files.

Wonderland Two – 4K HDR10 – 24fps – 51.4 Mb/s bitrate

First encoder comparison is with Samsung’s Wonderland Two 4K HDR10 video that is a time-lapse, where there are large chunks of the video that have small changes, while other parts have rapidly moving blurs. This one’s original bitrate is around 51,000k. We will be cutting it down to less than 1/20th it’s original size at 2500K, so there will be obvious quality lost. Let’s see how the encoders handle it!

VMAF scores - Wonderland Two

Both NVENC and QSV put up a great showing, riding the VMAF 93 mark at 7500K, a mere 15% of the original file size. VCN doesn’t even reach that point at 12500K, so would presumably need around twice as much bitrate to achieve the same quality!

Let’s take a deep dive into what these scores really translate too over the course of the movie. These charts are the VMAF scores at every 10 frames for each of the encoders with their 10000k bitrate video.

In this case the video is compressed to 5 times smaller than it’s original file size! It went on a diet from 821MB to 160MB, so expect to see some big encoding degradation.

VMAF breakdown chart for all encodings - wnderland two

It’s really obvious that AMD’s VCN encoder struggles with scene changes (the sudden sharp drops). I imagine this is due to lack of pre-analysis, as talked about in the last post. It also seems that QSV has some trouble with it, but at least NVENC has it’s head held high in that regard! I also suspect VCN is also trailing behind due to lack of B-frame support.

Dobly’s Glass Blowing Demo – 4K HDR10 – 60fps – 15.1 Mb/s bitrate

Second, Dobly’s Glass Blowing Demo is a three minute long 4K HDR10 video that is constant motion with lots of changing details, fire, smoke and rain. It is a high fps source video, but has much lower bitrate to start from. That means the scores should be higher, as it’s easier to reach the same quality.

Graph showing VMAF glass blowing demo comparison

AMD’s VCN and Nvidia’s NVENC trade blows the whole way with this video, with x265 taking a clear lead throughout the curve.

Intel QSV starts off rocking both NVENC and VCN then …something… happens. I honestly thought it was an error with my testing at first, possibly a misaligned video track while calculating VMAF. However after a quick look at the spread chat, we can see it’s more insidious than that.

VMAF breakdown chart for all encodings - glass blowing

There are two huge drops with QSV. I checked the video file, and found that there were two sections that became suddenly blocky and laggy, as if it was skipping or duplicating frames wrongly. I have no idea what caused it, and worse there was no indication of error! I can only speculate the encoder was designed to keep working even if there was some disruption in either compute capability or access to the video file. That is good for real-time encoding like streaming, but unacceptable for video archival.

This frankly leaves QSV out of the running for any consideration of use for backing up videos. If anyone knows of fixes or prevention of this, please leave a comment!

Samsung x RedBull: See the Unexpected – 4K HDR10 – 60fps – 51.8 Mb/s bitrate

Finally I tested with a high fps and high framerate video, Samsung x RedBull: See the Unexpected. I excluded testing QSV entirely with how bad it failed in the last run.

VAMF Chart - Samsung x RedBull

Nvidia’s NVENC is hot on x265’s tail, with VCN lagging behind.

VAMF breakdown for all encodings - Samsung x RedBull

AMD’s VCN still has clear drops. NVENC struggles more with this video than any of the others before, but still clearly pulls ahead of VCN. x265 is sitting tall, having a clean and very impressive line for a video that is a fifth of the original size with a minimum VMAF of 84.25 vs NVENC’s low of 56.13 then VCN taking rear guard at 48.16 (all had a max of 100).


Has HEVC hardware encoding caught up to the quality of software encoding?


It seems that the three titans of the GPU industry still haven’t figured out how to build encoding hardware pipelines that are both fast and high quality.

Would I use hardware encoders for my own videos?


Thee two Samsung tests were done on the extreme end of compressions, down to 1/20th of the original size. The Glass Blowing Demo VMAF deep dive gives a good idea of what would be more expected of a re-encode from going to 15Mb/s to 10Mb/s.

As we have said before, don’t needlessly re-encode videos. In my particular case I am happy using any of these hardware encoders for quick encodes rather that sitting around all day for a slightly, and probably unnoticeable, quality difference with x265.

Is there hope for a true consumer hardware encoding competitor to x265 quality?


Why would there be? Everything available is “good enough” and there is no incentive for these companies to spend the phenomenal effort into this specific task.

I would absolutely love to be proven wrong on this, but I personally don’t see any improvements being invested on HEVC encoding when AV1 is right around the corner.


These tests were done on my own hardware purchased myself. No company has not asked me to write this, modify or reword anything, nor omit anything. All conclusions are my own thoughts and opinions and in no way represent any company.

Raspberry Pi Hardware Encoding Speed Test

The GPU hardware encoder in the Raspberry Pi can greatly speed up encoding for H.264 videos. It is perfect to use for transcoding live streams as well. It can be accessed in FFmpeg with the h264_omx encoder. But is it fast enough for live stream a 1080p webcam?

You might have already seen a lot of people using the built-in raspberry pi cameras to stream crisp 1080p video, so why is this even a question? Well the catch there is the Pi Camera itself supports native H.264 encoding. Some webcams do as well, and they are honestly the best choice to use rather than constantly battering the GPU encoder if you don’t need to.

However, you may just happen to have an old cheap webcam that only does MJPEG streams. Those streams are generally too large to pump over the Raspberry Pi’s wifi at full fps. Would using the hardware encoder help you?

The Results First

This is why you’re here, let’s cut to the chase and do a comparison of the two latest Raspberry Pi’s available, the Pi 4 B, and Pi 3 B+ (we’ll throw in the little Pi Zero Wireless for fun too.) We’ll talk about the two videos used later, but suffice to say, Trackday is easier to encode and closer to what an average Webcam would produce. Artist is more of a torture test.

Boom! The Raspberry Pi 4 B is right in the butter zone. Most webcams that are 30fps would be handled just fine with the Pi 4 (depending on the quality of sensor and what you’re filming). The Pi 3 B+ isn’t terrible, but wouldn’t be able to encode a realtime stream smoothly.

The little Pi Zero? Well, it did its best and we’re proud of it!

Test Media


The first video I used was a video captured from a car on a racetrack. It is 1920×1080 at 30fps captured from a dash cam.

10 second preview of 2 minute video – Jaguar F-Type R at Harris Hills Raceway

The original bitrate was a 10.5MB/s and was cut down to 5MB/s with all our encodes.

The command used is:

ffmpeg -i trackday.mp4 -c:v h264_omx -b:v 5M -an -sn -dn track_omx.mp4


The second file, artwork in progress by Clara Griffith, is also 1920×1080 at 30fps. However it is using BT.709 color space and started out at 35MB/s!

Artwork of Clara Griffith – https://www.claragriffith.com/

If you see a webcam that advertises as “HDR” it is most likely using the BT.709 color space as well, and may give your Pi a headache.

This one was also compressed down to only 5MB/s. Why 5MB/s you ask? Well as it turns out, using the standard 2.4GHz wifi band, the Pi 3 and Pi 4 can each sustain about 6.5MB/s download speed over my wireless. That means I know these videos could be played smoothly over wifi. The Pi Zero W on the other hand could only sustain around 3MB/s wifi transfer speed.

All three systems were set up to use 256MB of GPU ram.

Video Quality

This actually took me by surprise to be honest. The quality of the encode is quite good when comparing to what a software encoder could do. I didn’t pull any punches either, the x264 encoder was set to dual pass and using veryslow preset with the film tune set. x264 commands:

ffmpeg -i "artist.mkv" -map 0:0 -c:v libx264 -pix_fmt yuv420p -tune:v film -color_primaries bt709 -color_trc bt709 -colorspace bt709  -pass 1 -passlogfile "pass_log_file_f9e11f23efaa23591fa8" -b:v 5000k -preset:v veryslow  -an -sn -dn -f mp4 /dev/null

ffmpeg -i "artist.mkv" -map 0:0 -c:v libx264 -pix_fmt yuv420p -tune:v film -color_primaries bt709 -color_trc bt709 -colorspace bt709  -pass 2 -passlogfile "pass_log_file_f9e11f23efaa23591fa8" -b:v 5000k -preset:v veryslow -map_metadata -1 -map_chapters 0  "artist-x264-5M-veryslow-film.mkv"

Of the two videos, Trackday is more realistic to what a webcam would experience and both encoders are near equal. So why was the Artist video so much better quality after encode, even though it started out with a lot higher bitrate? My informed guess on that is how crisp the original was, as well as the content is slow moving enough, the H.264 was able to reuse larger parts of the video for subsequent frames.

That means the software encoder x264 wins by virtue of being able to effectively use B-frames. Whereas the OMX hardware encoder doesn’t have support for B-frames. Therefor the Pi is on even ground when B-frames aren’t effective, but lags behind when they come into play.

A Note on Pi Camera Native H.264

I have found very little information about what Pi Cameras actually support H.264 natively. I only have “knock off” Raspberry Pi cameras that use the ribbon cable. They all support H.264 streams, which you can check with:

v4l2-ctl -d /dev/video0 --list-formats-ext

# ...
# [4]: 'H264' (H.264, compressed)
#                Size: Stepwise 32x32 - 2592x1944 with step 2/2
# [5]: 'MJPG' (Motion-JPEG, compressed)
#                Size: Stepwise 32x32 - 2592x1944 with step 2/2


ffmpeg -hide_banner -f video4linux2 -list_formats all -i /dev/video0

# [video4linux2,v4l2 @ 0x22c9d70] Raw       :     yuv420p :     Planar YUV 4:2:0 : {32-2592, 2}x{32-1944, 2}
# [video4linux2,v4l2 @ 0x22c9d70] Compressed:       mjpeg :            JFIF JPEG : {32-2592, 2}x{32-1944, 2}
# [video4linux2,v4l2 @ 0x22c9d70] Compressed:        h264 :                H.264 : {32-2592, 2}x{32-1944, 2}

I was kinda worried they were using some hackery to “pretend” to actually have native H.264 but instead using the GPU. However if the Pi Zero has anything to show, it has a really hard time encoding 1080p videos with the GPU encoder, so I do believe they have native support.

Wrap Up

If you already have:

A camera and a Raspberry Pi: you can get started streaming right away.

A 1080p webcam and want to stream from it: consider grabbing a Raspberry Pi 4.

The Raspberry Pi: first always try to grab a camera with built in H.264 support, otherwise, the Pi 4 should support most webcams using hardware accelerated encoding.

AMD Hardware Encoding in 2021 (VCE / VCN)

It’s 2021 and there still isn’t a lot of good info about AMD’s VCN hardware encoder for consumers. To that end, I will present my own take on the current “war” between software and hardware encoders, then go into quick details of how to best use AMD GPUs for encoding for video archival with FastFlix.

Note: I will only be comparing HEVC/H.265 10-bit HDR10 videos (both source and output). This use case is not usually covered in benchmarks and tests I have seen, and is more of the interest to those who have seen my previous posts on Encoding UHD HDR10 videos but may want to hardware accelerate it.


  • VCE – Video Coding Engine – AMD’s early name for its built in encoding hardware
  • VCN – Video Core Next – AMD’s new name for GPU hardware encoders (VCE / VCN used interchangeably)
  • AMF – Advanced Media Framework – AMD’s code and tools for developers to work with VCE / VCN
  • HEVC / H.265 – High Efficiency Video Coding – The videos codec we will use that supports HDR10
  • HDR10 – A set of metadata presented alongside the video to give the display additional details
  • VMAF – Netflix’s video quality metric used to compare encoded video to source’s quality

Software vs Hardware Encoders

Software encoders are coded to run on any general purpose CPU. Doesn’t matter if it’s an Intel i7, an AMD 5900x or even on your phone’s ARM based CPU. It gives them great versatility. In the other corner, Hardware encoders rely on specific physical hardware components to exist in a system to use them to accelerate their transcoding. In today’s case, it takes a AMD GPU with VCN support to use the features we want to test.

Apples and oranges are both fruit, sports cars and pickup trucks are both vehicles, and software and hardware encoders both transcode videos. Just as it’s futile to compare the track capabilities of a supercar to the towing capacity of a pickup truck, we are about to venture into said territory with these encoders.

Please excuse the poor artwork, Clara wasn’t available this week so I had to do it myself!

Use case over metrics

The workhorse of the HEVC software encoding world is x265. There are plenty of other software encoders like the industry used ATEME TITAN File for UHD blu-rays or other open source encoders like Turing codec or kvazaar, but because of their lack of inclusion in standard tools like FFmpeg, they are overlooked.

So what is this workhorse good for? Flexibility and video archival. By being able to run on almost anything that can compile C code, x265 is a champion of cross platform operations. It is also the standard when looking for pure quality metrics in HEVC videos.

Comparatively, hardware encoding, in this case using AMD’s Video Coding Engine (VCE), is built to be power efficient and fast. Really, really fast. For example, on a 6900XT you can real-time encode a 60fps UHD stream on the slowest setting!

Let’s see what happens when they venture into each other’s bailiwicks.

Drag Race

Here’s what everybody loves: A good graph. We’re going to compare x265 using it’s fastest encoding speed vs the slowest setting AMD’s VCE currently has with a 60fps HDR10 4K source video.

Using a 60fps HDR10 UHD source, x265 was compared with it’s highest speed preset vs VCE’s slowest

As expected, it was a slaughter. Hardware encoding ran at 96 fps while x265 could only manage 14.5 fps. AMD’s hardware encoding clearly pummels the fastest setting x265 has to offer, even on an i9-9900k. Even if using an AMD 5950x which may be up to twice as fast, the hardware encoder would still dominate.

Where does this matter

Streaming and real-time transcoding. Hardware encoders were designed with the idea of “accelerated” encoding. Which makes them great for powering your Zoom calls or streaming to Twitch.

Encoding Quality Prowess

Now lets venture into x265’s house and compare computed quality with VMAF. We’ll be using the veryslow setting, darn the total time taken!

In this scenario we will compress a UHD video with a bitrate of 15,000k to four different rates. The goal for a decent encode is to reach at least VMAF 93, which is the bitrate range we will stay above. (VMAF 93+ doesn’t mean you won’t notice quality loss. It simply means that it probably WILL BE apparent if it is less than that.)

This was tested with a 30 second excerpt from Dolby’s Glass Blowing Demo (UHD profile 8.1)

Both encoders do great, keeping within a range that shouldn’t be to noticeable. However, x265 has a clear advantage at lower bitrates if all you care about is quality. It also maintains a steady edge throughout the test.

I have noticed while watching the AMD VCE encodes that it doesn’t do a great job with scene changes. I expect that is because VCE doesn’t support pre-analysis for HEVC, only for H.264. AMD VCE also suffers from lack of B-frame support, which I will talk about in the next blog post.

Where does this matter

Video archival. If you have a video that you are planning to discard for a high quality re-encode to save on file size, it’s better to stick with x265. Keep in mind, don’t just re-encode because you want to use a “better” codec, it’s always best to keep the original.

Gas Guzzling

This is a comparison I don’t see as often, and I think is overlooked. Encoding takes a lot of power, which means it costs money. I have been told by many FastFlix users that they let their x265 encodes run overnight, and some of their encodings take days!

This is also a harder to measure metric, as you need both encoders to produce the same quality output, as well as know their power usage. The entire thing also labors under the assumption that the only purpose of this machine is to encode the video while it is powered on, so please keep all that in mind as we dive into this.

To achieve the same quality of result file, it costs ten times as much in electricity to get the job done. This may not matter if you’re talking about a random encode here or there, but if you have a lot of videos to burn through, it could really start saving cash by switching to hardware encoders.

The Nitty Gritty about the power (Methodology)

Power usage will differ across hardware so this is for a very specific case that I can attest for (using both HWmonitor and a KillAWatt monitor). The 6900XT uses 63 watts over it’s baseline when encoding, for a total system draw of ~320w. The i9-9900k uses 111 watts over baseline for a total system draw of ~360w. (Keep in mind there is some extra CPU usage during hardware encode as well, so that is why total power is not a direct difference between the two.)

For the encoder speed, when using a UHD file I was able to get within 0.1% difference of VMAF when using VCE slow (same speed as above) and x265 veryfast (at 10.35fps).

Lets take a genericized use case of a two hour long video running at 24fps. 24fps * 60 seconds in a minutes * 60 minutes in an hour * 2 hours = 172,800 frames.

Estimated times and cost:

  • VCE – slow – 6900XT @ 96.47fps – 29.85 minutes
    • 0.16 kWh/day @ 320 watts
    • 0.019$ at @12 cents per kWh
  • x265 – i9-9900K@ 10.35fps – 287.3 minutes (four and a half hours)
    • 1.72 kWh/day @ 360 watts
    • 0.206$ at @12 cents per kWh

Where does this matter

The cost difference probably doesn’t sway many individuals But if you’re a prolific encoder, this could save you time and money.

Super Technical Head to Head Summary

Software (x265)Hardware (AMD VCE)
Quality⭐Best possibleLacks basic HEVC needs (B-frames / pre-analysis)
SpeedSlow to Super Slow⭐Crazy Fast
Requirements⭐Any old electrified rockNewer AMD GPU
Windows OS
Energy UsageAll the powah!sips daintily

So the winner is…. neither. If you’re encoding professionally you’ll be working with totally different software (like TITAN File). Then if you’re using it at home, it really just depends with what hardware you already have. If you’re wondering which GPU to get for the best encoding, wait for next month’s article 😉

Basically they both do what they were designed for. I would say Hardware encoders might have a slight overall edge, as they could be used for all cases. Whereas x265 currently can’t do UHD HDR10 real time encoding on consumer hardware.

Encoding HDR10 with AMD GPUs

Already got an AMD GPU and want to start encoding with it? Great, let’s get down to how to do it. First off make sure you are using Windows. If you’re using Linux for this, don’t.* If Linux is all you have, I would still recommend using a passthrough VM with Windows on it.

For Windows users, rigaya has made a beautiful tool called VCEEncC that has HDR10 support built in. It is a command line tool, but good news, FastFlix now supports it!

You will need to download VCEEncC manually as well, and make sure it is on the system path or link it up in File > Settings of FastFlix.

VCE doesn’t have a lot of options to worry about like other encoders, so can be on your way to re-encoding in no time!

* Possible on Linux to using VAAPI to encode HEVC. You would need to apply custom MESA patches to enable HDR10 support. AMF / VCEEncC only supports H.264 on Linix currently.

Best quality possible with VCE

Beauty is in the eye of the beholder, and so is video quality. Some features, like VBAQ (Variance Based Adaptive Quantization) will lower the measured metrics like VMAF and SSIM, but are designed look better to human eyes. Assuming you care about how the video looks, and aren’t just trying to impress your boss with numbers, we will stick with those.

Motion Vector Accuracyq-pel

Of course the largest determination of quality will be how much bitrate you will allow for (or which quantization rate you select). FastFlix has some loose recommendations, but what is truly needed will vary greatly dependent upon source. A GoPro bike ride video will require a lot more bitrate than a mounted security camera with very little movement overall.

Warnings and gotchas

Not all features are available for all cards. Also some features like b-frame support were promised for RDNA2 but still are not yet available.

Driver versions can make a difference. Always try using latest first, but if you experience issues using VCE it may not be using a new enough AMF version and need to downgrade to an older driver.

What do I use?

Personally I avoid re-encoding whenever possible. However, now that I do have an AMD GPU I do use it for any of my quick and dirty encoding needs. Though I would be saying the same about NVENC if I had a new Nvidia GPU (which does have B-frame support). In my opinion it’s simply not worth the time and energy investment for encoding with software. Either save the original or use a hardware encoder.

What about Nvidia (NVENC) or Intel (QSV)?

I am working to get access to latest generation hardware for both Nvidia’s NVENC and Intel’s QSV in the next month, so hopefully I will be able to create a follow up with some good head to head comparison. Historically NVENC has taken the crown, and by my research VCE hasn’t caught up yet, but who knows where QSV will end up!

Boring Details

  • x265 was used at commit 82786fccce10379be439243b6a776dc2f5918cb4 (2021-05-25) as part of FFmpeg
  • CPU is a i9-9900k
  • VCEEncC 6.13 on 6900xt with AMF Runtime 1.4.21 / SDK 1.4.21 using drivers 21.7.2


These tests were done on my own hardware purchased myself. All conclusions are my own thoughts and opinions and in no way represent any company.

pre-commit – Check yourself before you wreck yourself

pre-commit checks are like prenups. They run before you commit, get the dirty stuff out of the way, and may even save your relationship(s) in the long run. The difference is that pre-commit checks are written by programmers and not lawyers, so they are a lot easier to read and implement.

There are several types of pre-commit checks that can be run. Some common features you could chose are:

  • git sanity checks (no huge files, no bad merge lines, etc…)
  • code style enforcement
  • whitespace fixes
  • python specific checks

Why pre-commit?

Just as you should do a quick check in the mirror before heading out, pre-commits are a quick reflection of your code before making it public. They quickly check everything you’re about to add to git to make sure it’s copasetic. Many even automatically fix common issues for you.

Basically pre-commits make sure your code’s tee-shirt isn’t on inside out.

How to pre-commit

There are three basic steps to get pre-commit working with your code repo.

  1. Install the pre-commit tool onto your system
  2. Add a pre-commit configuration file to your repo
  3. Install the git hook for the repo

1. Install pre-commit

There are several different official ways to install pre-commit. I personally do not like packages polluting my global python site-packages. Instead, I install it with user level only privileges. Then I make sure its install path is added to the system path.

pip install pre-commit --user

You will receive a warning that the script installation location is not on the system path. (Unless you have done this before, and then you can skip the next part.)

If you really don’t care and are very good about using isolated virtual environment, you can just install it globally with pip install pre-commit without the --user flag and skip the next part.


Installing collected packages: pre-commit
  WARNING: The scripts pre-commit-validate-config.exe, pre-commit-validate-manifest.exe and pre-commit.exe are installed in 'C:\Users\Chris\AppData\Roaming\Python\Python39\Scripts' which is not on PATH.

The above warning is what I received while installing this on windows. So I coped that path C:\Users\Chris\AppData\Roaming\Python\Python39\Scripts to my clipboard and then went through the following process to add it to my system path.

First, search for “edit system” on the window search bar and click the highlighted link.

Second, click on the “Environment Variables…” button at the bottom.

Third, in the bottom section under “System variables” click on “Path” and hit “Edit…”

Finally on the new window hit “New” in the top right and add the path previously copied to the clipboard. Then Hit Ok on all the windows to close them.

You will have to restart any cmd sessions you have opened. When you do, you should be able to run pre-commit just fine.


It’s a bit simpler to add the custom install location to the system path on linux.

INSTALL_PATH=/home/james/.local/bin  # Change to the path printed in your warning
echo "export PATH=\"${INSTALL_PATH}:\$PATH\"" >> ~/.bashrc 
source ~/.bashrc

2. The config file

In your repo, you will need to create a file named .pre-commit-config.yaml and chose which checks to add for your code. This is a config file that I use (with opinionated formatters and mypy removed). You can find the full list of built-in supported checks at the pre-commit github repo.

# .pre-commit-config.yaml
  - repo: https://github.com/pre-commit/pre-commit-hooks
    rev: v4.0.1

    # Identify invalid files
    - id: check-ast                        
    - id: check-yaml                       
    - id: check-json                       
    - id: check-toml                       

    # git checks
    - id: check-merge-conflict             
    - id: check-added-large-files          
      exclude: tests/media/.+
    - id: detect-private-key               
    - id: check-case-conflict              

    # Python checks
    - id: check-docstring-first            
    - id: debug-statements                 
    - id: requirements-txt-fixer           
    - id: fix-encoding-pragma              
    - id: fix-byte-order-marker            

    # General quality checks
    - id: mixed-line-ending                
    - id: trailing-whitespace              
      args: [--markdown-linebreak-ext=md]  
    - id: check-executables-have-shebangs  
    - id: end-of-file-fixer                

Personally I also always use black code formatter. It is not part of the standard checks, but is still easy to add.

  - repo: https://github.com/pre-commit/pre-commit-hooks
    rev: v4.0.1
       # ... 
  # Add at same level as the first pre-commit-hooks repo
  - repo: https://github.com/psf/black
    rev: 21.6b0 
      - id: black

3. Adding the pre-commit hook to git

Go into the repo with the config file at it’s root, and simply type:

pre-commit install

The first time you add it you will also want to run it on all the files in the repo to shore them all up (usually it only runs on the changed files).

pre-commit run --all-files

It will take a few minutes to install the virtual env for running these checks, but will be much faster after the first time. It should look something like:

pre-commit run --all-files
[INFO] Initializing environment for https://github.com/pre-commit/pre-commit-hooks.
[INFO] Initializing environment for https://github.com/psf/black.
[INFO] Installing environment for https://github.com/pre-commit/pre-commit-hooks.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/psf/black.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
Check python ast.........................................................Passed
Check Yaml...............................................................Passed
Check JSON...........................................(no files to check)Skipped
Check Toml...............................................................Passed
Check for merge conflicts................................................Passed
Check for added large files..............................................Passed
Detect Private Key.......................................................Passed
Check for case conflicts.................................................Passed
Check docstring is first.................................................Passed
Debug Statements (Python)................................................Passed
Fix requirements.txt.....................................................Passed
Fix python encoding pragma...............................................Passed
fix UTF-8 byte order marker..............................................Passed
Mixed line ending........................................................Passed
Trim Trailing Whitespace.................................................Failed
- hook id: trailing-whitespace
- exit code: 1
- files were modified by this hook

Fixing README.md
Fixing docs/README.md
Fixing docs/build-licenses.txt

Check that executables have shebangs.....................................Passed
Fix End of Files.........................................................Failed
- hook id: end-of-file-fixer
- exit code: 1
- files were modified by this hook

Fixing docs/build-licenses.txt
Fixing .pre-commit-config.yaml


4. Sit back and relax

Congrats, you are now totally almost protected from the most common silly mistakes you used to make. You now have the opportunity to make completely new mistakes you wouldn’t have thought of before!

Python’s New Structural Pattern Matching!

It’s finally happened, after years of complaining and told “it will never happen”, I got my darn switch statement! And honestly, calling it only a “switch” statement is really underselling it. It’s much more powerful than most people will ever use! You might have already heard about this, but I wanted to make sure it made it to at least the 3.10 beta phase, as the alphas are no guarantee you’ll actually see the new feature (still possible to remove before the RC phase, but less likely).

Artwork by Clara Griffith

Python 3.10 will introduce “Structural Pattern Matching” as introduced in PEP622 which is a crazy advanced switch statement that can recognizing patterns. Instead of the keyword switch Python will introduce match instead (get ready to update your regex variable names!). Let’s do a little quick compare with it and JavaScript’s switch statement. First let’s start off with the Javascript example modified from w3schools.

Javascript’s Switch

switch (new Date().getDay()) {
  case 5:
    console.log("It's Friday!");
  case 0:
  case 6:
    console.log("Woo, Weekend!");
    console.log("Work. Work. Work. Work.");

Python’s Structural Pattern Matching

We can accomplish the same with Python in less lines. Note that in Python the weekday starts at 0 for Monday.

from datetime import datetime

match datetime.today().weekday():  
    case 4:
        print("It's Friday!")
    case 5 | 6:
        print("Woo, Weekend!")
    case _:
        print("Work. Work. Work. Work.")

Similarities and Differences

With Python’s new match style statements, the biggest change from tradition is no “drop through” cases. Aka you don’t have to manually specify break or anything to exit the match case for every instance. That does mean though that you can’t have multiple matches with the same result. Instead you can combine checks with | for “or” operations. Which I honestly think is a lot cleaner.

What I don’t like is there is no default keyword or similar, and you have to use the _ wildcard. The underscore is already overused in my opinion. However it does add a lot of power for more advanced cases. Let’s dive into some more examples.

Matching and Assigning

The very basic things to understand about the new Structural Pattern Matching is the flow (as seen in the example above) and the possibility of assignment. Because not only can you make sure things are equal to another literal, you can detect patterns (as the name suggests) and then work with the variables inside the pattern itself.

Pattern Recognition

Lets dip the toes in on this whole “pattern matching” thing, what does that mean?

Say you have a tuple with two objects, that could be in either order and you always want to print it right.

data_1 = ("Purchase Number", 574)
data_2 = (574, "Purchase Number")

You could have a simple match case to straighten it out so it always prints “Purchase Number 574”. As the pattern matching support type checking.

match data_1:
    case str(x), int(y):
        print(x, y) 
    case int(x), str(y):
        print(y, x)

Match on dict values

Just like with regular value comparisons, you can check dictionary values and still do or operations. In this case, either grade b or c will print out "Welcome to being average!"

match {'grade': 'B'}:
    case {'grade': 'a' | 'A'}:
        print("You're a star!")
    case {'grade': 'b' | 'c' | 'B' | 'C'}:
        print("Welcome to being average!")

Unpacking arbitrary data

You can use the standard * and ** unpackers to capture and unpack variable length lists and dicts.

my_dict = {'data': [{'action': 'move'}, {'action': 'stay'}],
           'location': "square_10"}

match my_dict:
    case {'data': [*options], **remainder }:
    case _:
        raise ValueError("Data not as expected")

Will match with the first case and print out:

[{'action': 'move'}, {'action': 'stay'}]
{'location': 'square_10'}

That can really come in handy if dealing with variable length data.

Adding the “if”

You can also add in some inline checks.

cords = (30.25100365332043, -97.86221371827051, "Workplace")
cords_2 = (44.40575746530253, 8.947747627912994)

match cords: 
    case lat, lon, *_ if lat > 0:
        print("Northern Hemisphere")
    case lat, lon, *_ if lat < 0: 
        print("Sothern Hemisphere")    

Don’t forget about “as”

Sometimes you might want one the many literals you are looking for to be usable in the case itself. Lets put a lot of our knowledge together and check out the added power of the as clause.

We are going to build a really simple command line game to look for random things.

import sys
import random

inventory = []

while True:
    match input().lower().split():
        # Remember `split` makes the input into a list.  
        # These first two cases are functionally the same.
        case ["exit"]:  
        case "stop", *_: 
        case "travel", "up" | "down" | "left" | "right" as direction:
            print(f"Going {direction}")
        case "search", "area" | "backpack" as thing, _, *extra:
            item = " ".join(extra)
            if thing == "backpack":
               if item  in inventory:
                   print(f"Yes you have {item}")
                   print(f"No you don't have {item}")
            elif thing == "area":
                 if random.randint(1, 10) > 5:
                     print(f"You found {item}!")
                    print(f"No {item} here")
                print(f"Sorry, you cannot search {thing}")
        case _:
            print("sorry, didn't understand you! type 'exit' or 'stop' to quit")

Lets do a quick playthrough:

> search area for diamonds
You found diamonds!

> search backpack for diamonds
Yes you have diamonds

> take a short rest and try to eat the diamonds
sorry, didn't understand you! type 'exit' or 'stop' to quit

> Travel Up
Going up

> search area for candy
No candy here

> search backpack for candy
No you don't have candy

> stop
# Process finished with exit code 0

Notice our special case for searching around.

case "search", "area" | "backpack" as thing, _, *extra:

we are looking for the first keyword “Search”, then either “area” or “backpack” and saving which to the variable thing. The _ will ignore the next word as we expect them to type for or something useless there. Finally we grab everything else and treat it as a single item.

Classes and More

Using dataclasses with match is super easy.

from dataclasses import dataclass

class Car:
    model: str
    top_speed: int

car_1 = Car("Jaguar F-Type R", 186)
car_2 = Car("Tesla Model S", 163)
car_3 = Car("BMW M4", 155)

match car_1:
    case Car(x, speed) if speed > 180:
        print(f"{x} is a super fast car!")
    case Car(x, speed) if speed > 160:
        print(f"{x} is a pretty fast car")
    case _:
        print("Regular Car")

Using a regular class, you have to be a pit more explicit about the varaibles that are being used.

class Car:

    def __init__(self, model, top_speed):
        self.model = model
        self.top_speed = top_speed

car_1 = Car("F-Type R", 186)
car_2 = Car("Model S", 163)
car_3 = Car("BMW M4", 155)

match car_1:
    case Car(model=x, top_speed=speed) if speed > 180:
        print(f"{x} is a super fast car!")
    case Car(model=x, top_speed=speed) if speed > 160:
        print(f"{x} is a pretty fast car")
    case _:
        print("Regular Car")

However, there is a way around that! You can add __match_args__ to the class itself to define which arguments you will want to use for the pattern recognition.

class Car:

    __match_args__ = ["model", "top_speed"]
    def __init__(self, model, top_speed):
        self.model = model
        self.top_speed = top_speed

car_1 = Car("F-Type R", 186)
car_2 = Car("Model S", 163)
car_3 = Car("BMW M4", 155)

match car_1:
    case Car(x, speed) if speed > 180:
        print(f"{x} is a super fast car!")
    case Car(x, speed) if speed > 160:
        print(f"{x} is a pretty fast car")
    case _:
        print("Regular Car")


Structural Pattern Matching is Python’s sexy new “switch” statement that will bring a lot of power, and removal of a lot of old if elif blocks. The only real downside is that it’s brand new, and will take a few years for most people to be able to use it with their default interpreter.