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.

Terms:

  • 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.

Presetslow
Motion Vector Accuracyq-pel
VBAQenabled
Pre-Encodeenabled

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

Disclaimer

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.

Windows

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.

Linux

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
repos:
  - repo: https://github.com/pre-commit/pre-commit-hooks
    rev: v4.0.1
    hooks:

    # 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.

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

black....................................................................Passed

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!");
    break;
  case 0:
  case 6:
    console.log("Woo, Weekend!");
    break;
  default:
    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 }:
        print(options)
        print(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"]:  
            sys.exit(0)
        case "stop", *_: 
            sys.exit(0)
        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}")
               else:
                   print(f"No you don't have {item}")
            elif thing == "area":
                 if random.randint(1, 10) > 5:
                     inventory.append(item)
                     print(f"You found {item}!")
                 else:
                    print(f"No {item} here")
            else:
                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

@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")

Summary

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.

Stop Re-Encoding Videos!

I know this may sound like a weird statement coming from the author of FastFlix, but from the bottom of my heart, please stop the needless pixel loss! Every time you re-encode (aka transcode) a video it losses information, which lowers the quality and makes it a lot worse if you ever need to do it again. Re-encoding makes you a pixel killer!

Why am I saying re-encoding and not just “encoding” when you may have the original video? Sadly, to even fit onto your computer or device the video you are working with has already been encoded in a highly compressed manor. Even phones and professional cameras like the RED series use real-time compression. Raw video takes too much bandwidth for standard storage media. For example, imaging recording raw video using a video sensor at 16-bit 60FPS. A single minute of UHD footage would be near 60GB! That translates into a bitrate of over 7,500,000 kbps. Compare that to a re-encoded YouTube video of HDR 60FPS 4K footage is around 30,000 kbps, 250x times less quality!

Why does it matter?

Everyone has seen potato quality images and videos being re-shared again and again. The quality loss is due to needless re-encoding.

Websites generally always re-encode videos for a variety of reasons, such as adding watermarks or forcing certain bitrates. It may be required in some instances, but for most cases it’s overkill. If I had the power I would challenge websites to instead publish what encoding targets are required and allow for direct playback of the original file.

But while we don’t have control over that, that makes it even more important to not make it worse when you have control over it.

In the above example I took a short video and encoded it again and again and again using the same settings each time. The video still looks good while being watched, but if you zoom in you can see the entire thing has essentially become blurred. Don’t do this to your own videos, save the pixels!

Future Proofing

Another thing to consider is not just the result you have now, but how it will look in ten or twenty years. Sure, the single 1080p or 4K re-encode you did now may look perfect. But what about when 16K TVs are standard? What about when your phone or monitors pixel density is four to eight times higher than it is now? Behind the scenes of good TVs and devices is an “Ai” chip that upscales videos to look better on newer screens. With less detail to start with, it won’t look as good as if you didn’t re-encode.

Remember that amazing video you took with your flip phone all those years ago that you can’t even tell what is happening in it anymore? You don’t want that to happen to your videos now.

There are some cases that it cannot be avoided that we will cover later, but even common operations like trimming videos and rotating them can be accomplished without re-encoding.

Trim and Rotate without re-encoding

Two very common reasons people want to re-encode videos is to shorten them to a particular section, or rotate it the proper direction. Thankfully you can do both of those without modifying the original video stream. I am using the command line tool ffmpeg to accomplish this, which is available for free.

Rotate

To rotate the video, you just have to add some metadata to the container the video is in. This is how phones set videos to portrait or landscape mode without having to change their encoder settings.

ffmpeg -i your_video.mp4 -metadata 0:s:v rotate=90 -map 0 -c copy rotated_video.mp4

In the above example, replace your_video.mp4 with the video file you need rotated. It will be copied with the new metadata to rotated_video.mp4 and now should be rotated properly.

Trim

To cut out a section of the video, you simply need to “copy” everything between the two desired points. For example if you want to cut out a 48 second section between 1:02 and 1:50 (one minute two seconds and one minute fifty seconds), use the following command.

ffmpeg --ss 1:02 -to 1:50  -i your_video.mp4 -map 0 -c copy rotated_video.mp4

Are there any exceptions?

There are a few cases where you simply cannot avoid re-encoding. If you need to add effects, crop, make any actual modifications to the video, you will need to re-encode.

However if you are keeping the video as is, there are only three instances you should consider re-encoding for:

  • Limited bandwidth scenarios
  • Device compatibility
  • Cannot provide required storage space

Limited bandwidth scenario

ISPs still like to pretend upload speeds don’t matter. Even if an ISP provides 1Gbps down most still have less than 45Mbps upload peak. Things get even worse for mobile, where the average upload rate is around 10Mbps. Then on top of that, a general rule of thumb is your video bitrate should be around half or less of available bandwidth to ensure there isn’t stuttering.

That means if you’re planning to share videos in real time with the world, you simply have to re-encode (transcode) it.

Device compatibility

There are some large companies that have terrible compatibility with commonly accepted formats to be anti-competition. That means if you or a loved one is enslaved to some large fruit company, you may need to tinker with your videos to please the orchard overlords.

Storage space

“Storage is cheap” is a phrase I hear a little too much in the coding world. In the real world without a large quarterly tech budget, you have to count your pennies. If you can re-encode a video so that it’s near visually lossless while saving on storage space for your needs, go for it!

16TB Showdown – WD HC550 vs Seagate EXOS 16 vs Toshiba MG08

Hard Drive Interals

The three cheapest SATA 16TB drives available at the moment (March 2021) are all around $350: the Seagate EXOS 16, the WD Ultrastar DC HC550 and the Toshiba MG08 Series. I planned on using two of each in my home NAS, until a shocking discovery while doing a quick benchmark on them! To spoil the suspense, the Toshiba was either defective or was designed with different workloads in mind.

A quick note: these were all purchased out of pocket for my own setup. I do not have any sponsors, nor any advertising on my website. These results are purely my own findings. I simply wanted to share to those who may be interested.

The Contenders

As you may have guessed, these 16TB drives have a lot in similar. Each drive is helium filled, 7200rpm and SATA III (6.0Gb/s). The three of them also offer 2.5M hours of Mean Time Between Failures and max 550TB/yr workload. There are a few subtitle differences though.

Seagate
EXOS 16
WD Ultrastar
DC HC550
Toshiba
MG08 Series
Cache256mb512mb512mb
Block512e / 4Kn512e / 4Kn512e OR 4Kn
TechCMRCMR / EAMRCMR

They also sport a few physical design differences. The WD seems to be “upside down” compared to the others, and the standard middle side screw hole did not line up in my case.

For those who are design conscious, the EXOS is the only one offering a splash of color, and none of them offer RGB lights. Though if you are looking for RGB lights on a HDD, you might need a mental recalibration via wrench to the side of the head. HDDs are like construction workers, they work all day and are only noticed when they are slowing things down.

The Test Machine

This system was not designed to pump out the best benchmark performance. This is a real world NAS I will be using for my house. That said, it may not eek out the most performance on these drives, but it does give us a standard base for comparison.

  • Operating System: TrueNAS SCALE 21.02 Alpha
  • CPU: Ryzen 5 5900x 12-core 3.7GHz
  • Motherboard: ASRock X570 PHANTOM GAMING 4
  • Memory: NEMIX RAM 64GB DDR4-3200 P 2Rx8 ECC Unbuffered Memory
  • PSU: CORSAIR RM Series RM750
  • HBA: Dell H310 LSI IT mode 9211-8i

The hard drives were all connected to the HBA card and tested one at a time. They were setup into single ZFS pools using the TrueNAS UI with record sizes set to 512K.

All three drives are the exact same size (perfect for a NAS setup) and using the same 512e block size.

14.55 TiB, 16000900661248 bytes, 31251759104 sectors
Units: sectors of 1 * 512 = 512 bytes
Sector size (logical/physical): 512 bytes / 4096 bytes
I/O size (minimum/optimal): 4096 bytes / 4096 bytes

Benchmark Results

Read Speeds

These were gathered by running hdparm -tT twice on each drive and taking the better of the two (they were extremely similar and didn’t seem to merit more tests).

They were all so close we really need to “zoom in” on the differences.

That’s a bit better. However it exaggerates the real world difference because the lack of scale, so keep that in mind (never trust a graph that doesn’t start at zero!) Out of the three, Seagate EXOS 16 looks to be the sweet spot for cached and buffered read speeds. The other two both put in great numbers for spinning platter disks, and may want to stick to the WD if you will be working with frequently cached data.

Write Speeds

Each Benchmark for the write speeds were done using the dd command three times each to pump output from /dev/urandom to each drive. The tests will not encounter compression or caching done during the runs with this design. This means these numbers are for comparison to each other only and not max speed. Each drive was in a single ZFS vdev by itself with a record size of 512k.

Large blocks

Here is where the Toshiba MG08 started to really worry me. Its write speed of large 1M block sizes is half that of the others.

dd if=/dev/urandom  of=/mnt/<dive>/tmp.file  bs=1M  count=1000  oflag=dsync

Medium blocks

The Seagate and WD are still leading by a considerable amount, but this seems to be the most favorable benchmark for the Toshiba.

dd if=/dev/urandom  of=/mnt/<dive>/tmp.file  bs=4096k  count=1000  oflag=dsync

Small blocks

Full on nosedive for the Toshiba, over four times slower than the other drives.

dd if=/dev/urandom  of=/mnt/<drive>/tmp.file  bs=4096  count=1000  oflag=dsync

After reviewing the data, I am convinced that the drive is either defective or is designed for a different type of workload than what is in my setup. I did reach out to other enthusiasts on the /r/homelab discord and nothing seemed to cause that dip due to methodology at least.

Conclusions

Both the WD Ultrastar HC550 and Seagate EXOS 16 seem to be good choices for a home NAS. Others seem to also have great performance with the Toshiba MG08, that I was not able to duplicate in this instance. I wish I had the capability to grab another Toshiba to see if this was just a defective drive or my setup, but I can’t just throw cash at it.

For now I am putting together a combo of WDs and Seagates into my NAS and calling it good. Hope you found this information useful!