Good Code Design From Linux/Kernel

Learn how Linux/FFmpeg C partial codebase is organized to be extensible and act as if it were meant to have “polymorphism”. Specifically, we’re going to briefly explore how Linux concept of everything is a file works at the source code level as well as how FFmpeg can add support fast and easy for new formats and codecs.

diagram_components

 

Good software design – Introduction

To write useful and long term maintainable software we tend to look out for patterns and group them into abstractions and it seems that’s the case for devs behind Linux and FFmpeg too.

Software design

When we’re creating software, we’re building data structures and defining their behaviors and dependencies. The way we create and link them can be seen as the design/architecture of the software.

Let’s say we’re building a media framework that encodes/decodes video and audio. The codecs AV1, H264, HEVC, and AAC all do some common operations and if we can provide a generic abstraction that holds these common operations and data we can use this concept instead of relying on the concrete idea of what a specific codec does.

Through the years many developers noticed that software with a good design is a good idea that pays off as software grows in complexity.

This is one of the ideas behind the good design for software, to rely on components that are weakly linked and with boundaries around what it should do.

Ruby

Maybe it’s easier to see all these concepts in practice. Let’s code a quick pseudo media stream framework that provides encoding and decoding for several codecs.

This pseudo-code in ruby tries to recreate what we’re discussing above, there is an implicit concept here of what operations a codec must have, in this case, the operations are encode and decode. Since ruby is a dynamically typed language any class can present these two operations and act as a codec for us.

Developers sometimes may use the words: contract, API, interface, behavior and operations as synonyms.

This design might be considered good because if we want to add a new codec we just need to provide an implementation and add it to the list, even the list could be built in a dynamic way but the idea is that this code seems easy to extend and maintain because it tries to keep link between the components weak (low coupling) and each component does only what it should do (cohese).

Rails framework even enforce some way to organize the code, it adopts the model-view-controller (MVC) architecture

Golang

When we go (no pun intended) to a statically typed language like golang we need to be more formal, describing the required types but it’s still doable.

The interface type in golang is much more powerful than Java’s similar construct because its definition is totally disconnected from the implementation and vice versa. We could even make each codec a ReadWriter and use it all around.

Clang

In the C language we still can create the same behavior but it’s a little bit different.

Code inspired by https://www.bottomupcs.com/abstration.xhtml

We first define the abstract operations (functions in this case) in a generic struct and then we fill it with the concrete code, like the av1 decoder and encoder real code.

Many other languages have somewhat similar mechanisms to dispatch methods or functions as if they were part of an agreed protocol and then the system integration code can deal only with this high-level abstractions.

Linux Kernel – Everything is a file

Have you ever heard the expression everything is a file in Linux? The idea is to have a common interface for all kinds of resources in Linux, for instance, Linux handles network socket, special files (like /proc/cpuinfo) or even USB devices as files.

This is a powerful idea that can make easy to write or use programs for linux since we can rely in a set of well known operations from this abstraction called file. Let’s see this in action:

This only is possible because the concept of a file (data structure and operations) was design to be one of the main way to communicate among sub-systems. Here’s a gist of the file_operations’ API.

The struct file_operations define what one should expect from a concept of what file can do.

Here we can see the directory implementation of these operations for the ext4 file system.

And even the cpuinfo proc files is done over this abstraction. When you’re operating files under linux you’re actually dealing with the VFS system, this system delegates to the proper implementation file implemenation.

Screen Shot 2019-08-21 at 10.14.07 AM

Source: https://ops.tips/blog/what-is-that-proc-thing/

FFmpeg – Formats

Here’s an overview of FFmpeg flow/architecture that shows that the internal componets are linked mostly to the abstract concepts like AVCodec but not directly to their implemenation, H264, AV1 or etc.

remuxing_libav_components
FFmpeg architecture view from transmuxing flow

 

For the input files, FFmpeg creates a struct called AVInputFormat that is implemented by any format (video container) that wants to be used as an input. MKV files fill this structure with its implementation as the MP4 format too.

 

This design allows new codecs, formats, and protocols to be integrated and released easier. DAV1d (an av1 open-source implementation) was integrated into FFmpeg May this year and you can follow along the commit diff to see how easy it was. In the end, it needs to register itself as an available codec and follow the expected operations.

No matter the language we use we can (or at least try to) build a software with low coupling and high cohesion in mind, these two basic properties can allow you to build easier to maintain and extend software.

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Use URL.createObjectURL to make your videos start faster

faster-start-up

During our last hackathon, we wanted to make our playback to start faster. Before our playback starts to show something to the final users we do around 5 to 6 requests (counting some manifests) and our goal was to cut as much as we can.

Screen Shot 2018-08-10 at 8.55.20 PM

The first step was very easy, we just inverted the code logic from the client side to the server side and then we injected the prepared player on the page.

Pseudo Ruby server side code:

some_api = get("http://some.api/v/#{@id}/playlist")
other_api = get("http://other.api/v/#{@some_api.id}/playlist")
# ...
@final_uri = "#{protocol}://#{domain}/#{path}/#{manifest}"

Pseudo JS client side code:

new Our.Player({source: {{ @final_uri }} });

Screen Shot 2018-08-10 at 8.57.13 PM

Okay, that’s nice but can we go further? Yes, how about to embed our manifests into our page?! It turns out that we can do that with the power of URL.createObjectURL, this API gives us an URL for a JS blob/object/file.

// URL.createObjectURL is pretty trivial
// to use and powerfull as well
 var blob = new Blob(["#M3U8...."]
            , {type: "application/x-mpegurl"});
 var url = URL.createObjectURL(blob);

Pseudo Ruby server side code:

some_api = get("http://some.api/v/#{@id}/playlist")
other_api = get("http://other.api/v/#{@some_api.id}/playlist")
# ...
@final_uri = "#{protocol}://#{domain}/#{path}/#{manifest}"
@main_manifest = get(@final_uri)
@sub_manifests = @main_manifest
                 .split_by_uri
                 .map {|uri| get(uri)}

Pseudo JS client side code:

  var mime = "application/x-mpegurl";
  var manifest = {{ @main_manifest }};
  var subManifests = {{ @sub_manifests }};
  var subManifestsBlobURL = subManifest
                           .splitByURL()
                           .map(objectURLFor(content, mime));
  var finalMainManifest = manifest
                          .splitByLine()
                          .map(content.replace(id, subManifestsBlobURL[id]))
                          .joinWithLines();

  function objectURLFor(content, mime) {
    var blob = new Blob([content], {type: mime});
    return URL.createObjectURL(blob);
  }

  new Our.Player({
    src: objectURLFor(finalMainManifest, mime)
  })

Screen Shot 2018-08-10 at 8.57.43 PM

We thought we were done but then we came up with the idea of doing the same process for the first video segment, the page now will weight more but the player would almost play instantaneously.

// for regular text manifest we can use regular Blob objects
// but for binary data we can rely on Uint8Arrary
var segment = new Uint8Array({{ segments.first }});

By the way, our player is based on Clappr and this particular test was done with hls.js playback which does use the fetch API to get the video segments, fetching this created URL works just fine.

The animated gif you see at the start of the post was done without the segment on the page optimization. And we just ignored the possible side effects on the player ABR algorithm (that could think it has a high bandwidth due to the fast manifest fetch).

Finally, we can make it even faster using the MPEG Dash and its template timeline format, we can use shorter segments sizes and we can tune the ABR algorithm to be initially faster.

How to measure video quality perception

Update 2(01/06/2016): Fixed reference video bitrate unit from Kbps to KBps

Update 1(10/16/2016): Anne Aaron presented the VMAF at the Demuxed 2016.

When working with videos, you should be focusing all your efforts on best quality of streaming, less bandwidth usage, and low latency in order to deliver the best experience for the users.

This is not an easy task. You often need to test different bitrates, encoder parameters, fine tune your CDN and even try new codecs. You usually run a process of testing a combination of configurations and codecs and check the final renditions with your naked eyes. This process doesn’t scale, can’t we just trust computers to check that?

bit rate (bitrate): is a measure often used in digital video, usually it is assumed the rate of bits per seconds, it is one of the many terms used in video streaming.

screen-shot-2016-10-08-at-9-30-26-am
same resolution, different bitrates.

codec: is an electronic circuit or software that compresses or decompresses digital content. (ex: H264 (AVC), VP9, AAC (HE-AAC), AV1 and etc)

We were about to start a new hack day session here at Globo.com and since some of us learned how to measure the noise introduced when encoding and compressing images, we thought we could play with the stuff we learned by applying the methods to measure video quality.

We started by using the PSNR (peak signal-to-noise ratio) algorithm which can be defined in terms of the mean squared error (MSE) in decibel scale.

PSNR: is an engineering term for the ratio between the maximum possible power of a signal and the power of corrupting noise.

First, you calculate the MSE which is the average of the squares of the errors and then you normalize it to decibels.

For 3D signals (colored image), your MSE needs to sum all the means for each plane (ie: RGB, YUV and etc) and then divide by 3 (or 3 * MAX ^ 2).

To validate our idea, we downloaded videos (720p, h264) with the bitrate of 3400 kbps from distinct groups like News, Soap Opera and Sports. We called this group of videos the pivots or reference videos. After that, we generated some transrated versions of them with lower bitrates. We created 700 kbps, 900 kbps, 1300 kbps, 1900 kbps and 2800 kbps renditions for each reference video.

Heads Up! Typically the pivot video (most commonly referred to as reference video), uses a truly lossless compression, the bitrate for a YUV420p raw video should be 1280x720x1.5(given the YUV420 format)x24fps /1000 = 33177.6KBps, far more than what we used as reference (3400KBps).

We extracted 25 images for each video and calculate the PSNR comparing the pivot image with the modified ones. Finally, we calculate the mean. Just to help you understand the numbers below, a higher PSNR means that the image is more similar to the pivot.

700 kbps 900 kbps 1300 kbps 1900 kbps 2800 kbps 3400 kbps
Soap Op. 35.0124 36.5159 38.6041 40.3441 41.9447
News 28.6414 30.0076 32.6577 35.1601 37.0301
Sports 32.5675 34.5158 37.2104 39.4079 41.4540
screen-shot-2016-10-08-at-9-15-24-am
A visual sample.

We defined a PSNR of 38 (from our observations) as the ideal but then we noticed that the News group didn’t meet the goal. When we plotted the News data in the graph we could see what happened.

The issue with the video from the News group is that they’re a combination of different sources: External traffic camera with poor resolution, talking heads in a studio camera with good resolution and quality, some scenes with computer graphics (like the weather report) and others. We suspected that the News average was affected by those outliers but this kind of video is part of our reality.

kitbcrnx2uuu4
The different video sources are visible in clusters. (PSNR(frames))

We needed a better way to measure the quality perception so we searched for alternatives and we reached one of the Netflix’s posts: an approach toward a practical perceptual video quality metric (VMAF). At first, we learned that PSNR does not consistently reflect human perception and that Netflix is creating ways to approach this with the VMAF model.

They created a dataset with several videos including videos that are not part of the Netflix library and put real people to grade it. They called this score of DMOS. Now they could compare how each algorithm scores against DMOS.

netflix
FastSSIM, PSNRHVS, PSNR and SSIM (y) vs DMOS (x)

They realized that none of them were perfect even though they have some strength in certain situations. They adopted a machine-learning based model to design a metric that seeks to reflect human perception of video quality (a Support Vector Machine (SVM) regressor).

The Netflix approach is much wider than using PSNR alone. They take into account more features like motion, different resolutions and screens and they even allow you train the model with your own video dataset.

“We developed Video Multimethod Assessment Fusion, or VMAF, that predicts subjective quality by combining multiple elementary quality metrics. The basic rationale is that each elementary metric may have its own strengths and weaknesses with respect to the source content characteristics, type of artifacts, and degree of distortion. By ‘fusing’ elementary metrics into a final metric using a machine-learning algorithm – in our case, a Support Vector Machine (SVM) regressor”

Netflix about VMAF

The best news (pun intended) is that the VMAF is FOSS by Netflix and you can use it now. The following commands can be executed in the terminal. Basically, with Docker installed, it installs the VMAF, downloads a video, transcodes it (using docker image of FFmpeg) to generate a comparable video and finally checks the VMAF score.

You saved around 1.89 MB (37%) and still got the VMAF score 94.

Using a composed solution like VMAF or VQM-VFD proved to be better than using a single metric, there are still issues to be solved but I think it’s reasonable to use such algorithms plus A/B tests given the impractical scenario of hiring people to check video impairments.

A/B tests: For instance, you could use X% of your user base for Y days offering them the newest changes and see how much they would reject it.

Functional Programing 101 :: WWH

function-machine

WWH: What? Why? How?

  1. What: a quick (hopefully, useful to real world) guide to functional programing using JavaScript strongly based on most adequate book.
  2. Why: it might empower you to write more robust programs: reusable, shorter, easier to reason about, less prone to error among others.
  3. How: by providing a quick textual introduction (WWH) followed by a simple code example and when possible a real code example.

Intro :: concepts

Functional Programing

What: a way to build code in which you use functions as the main design tool.

Why: might lead to code that’s easier to test, debug, parallelize, and understand.

How: thinking about what programs should do instead of how, using functions as the major unit to solve problems on computer.

First Class Functions

What: “functions are like any other data type and there is nothing particularly special about them – they may be stored in arrays, passed around, assigned to variables.”

Why: use functions to compose programs in a style that you can easily reason about, maintain, reuse and grow.

How: just create and use functions to solve problems.

Pure Functions

What: “a function that, given the same input, will always return the same output and does not have any observable side effect.”

Why: with pure functions we can easily cache, debug, test and parallelize the processing of them. There is no state to understand / set up.

How: write functions that does not have side effect. Although we’ll eventually write programs that mutate values, we can certainly try to minimize it. (And when we do need to mutate values, we can use functions to help us)

Basic toolbox :: currying

What: “You can call a function with fewer arguments than it expects. It returns a function that takes the remaining arguments.”

Why: you can promote the reusability to function level, you can use them to compose programs that expects another function

How: build a function with n parameters that returns n functions instead of the immediate result.

Medium toolbox :: composing

What: is the act of creating your programs using lots of functions.

Why: this promotes the reuse at a great level and forces you to think about what instead of how.

How: chain functions to produce a new callable function.

Example :: motivational

What: a better example to motivate you to go further with functional programing.

Why: most near real world examples are great to motivate you to learn something.

How: since you can see all the concepts together, I think you’ll notice the value.

You can see the example running at https://jsfiddle.net/swmrmgur/2/ and check the commented code down bellow.

Screen Shot 2016-04-27 at 2.17.19 PM

Advanced toolbox & conclusion

I hope you might see the benefits you can have from using one or other technique from functional programming but for sure there are other benefits not shown here, I strongly recommend you to read the INCREDIBLE free book (gitbook) “Professor Frisby’s Mostly Adequate Guide to Functional Programming”, in fact, most of the ideas and examples here are from it.

There are advanced techniques to deal with data mutation with less pain, to handle errors and exceptions without try and catch and more abstractions that can help you and you can read them on the book.

And don’t use the handcrafted curry and compose built here (they’re far from production-ready), instead use a library like Ramda, which provides many basic functions like: map, filter and other all of them already curried, or lodash-fp.

Yeah, there no monado here. A special thank to Daniel Martins and Juarez Bochi, they helped a lot.

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