Principle and performance analysis of lepton lossless compression

author ：vivo Internet database team - Li Shihai

This paper mainly introduces the outline process and principle of lossless compression pictures , as well as Lepton The problems and solutions of lossless compression found in the previous research .

One 、 Start with a game

1.1 Game fault finding

Please take out your stopwatch to time , stay 15 Find out the difference between the following pictures in seconds .

time out , Did you find the difference between the two pictures ？

Two 、 The growth of the wise

In the game above , You may not find any difference between the two pictures , In fact, one of them is 3.7MB Of jpg Original drawing of format , The other one is of size 485KB Of jpg Format compressed pictures , It's just different sizes . You may be a little angry , Indignant to this is cheating , However, smart you soon have a series of questions in your brain , These question marks let you uncover the veil of the game layer by layer , No longer regret for fooling , Instead, get happiness from new knowledge .

2.1 Socrates: midwifery

• Why does the picture above become smaller ？
• Where is the lost information ？
• Why is the picture quality declining , I can't see it ？
• Can I make it smaller ？
• Can I restore it to its original size ？
• Why compress my pictures ？

Why is the picture above smaller ？ Picture from 3.7MB become 485KB It's because I used the image viewing tool to save the original image as a new image , In the process of saving , There is a parameter for image quality selection , I chose the lowest quality , After saving, a smaller picture is generated . But the quality of the picture has declined , Why can't you see ？ This requires understanding the principle of image compression .

2.2  Explore the story behind the appearance

Take advantage of the weakness of the human eye .

There are two kinds of cells in human retina , Cones and rods . Cone cells are used to sense color , Rod cells are used to sense brightness . And relative to color , Our perception of light and dark is more obvious . Therefore, color information can be compressed to reduce the size of the picture . So we will transform the color space before image compression ,JPEG Pictures are usually transformed into YCbCr Color space ,Y For brightness ,Cb Blue chroma ,Cr Red chroma , After the transformation, it is easier for us to deal with the color part . Then we cut a picture into pieces 8*8 Pixel block of , Then use the discrete cosine transform algorithm (DCT) Calculate the high frequency region and low frequency region . Because the human eye is not sensitive to complex information in the high-frequency region , Therefore, this part can be compressed , This process is called quantification . Finally, package the new file . This process completes the image compression . The basic flow chart is as follows ：

JPEG Compression is lossy .

In the above process , After the color space conversion of the prediction module , By discarding some color density information , Increase the compression ratio . Common options are 4:2:0, After this step, we need 8 Information represented by numbers , Now it's just a matter of 2 individual , Directly abandoned 75% Of Cb Cr Information , However, this step is irreversible , This leads to the loss of image compression . In addition, in the entropy coding module , Will further use stroke length coding or Huffman Encoding further compresses the picture information , And the compression of this part is lossless , It's reversible .

（YCbCr Space conversion ）  The principle of Huffman coding is as follows ： If the total number of characters to be encoded is 38 Symbol data , Count them , The symbols and corresponding frequencies obtained are shown in the following table ：

First , Sort all symbols by frequency , After sorting, see the following figure ：

then , Select two leaf nodes with the lowest frequency , The one with the least frequency is the left child node , The other is the right child node , The root node is the sum of the frequencies of the two leaf nodes .

（Huffman Trees ）  Go through the above steps , It forms a Huffman Trees ,Huffman Coding is often used in lossless compression , Its basic idea is to use short encoding to represent characters with high frequency , Use long encoding to represent characters with low frequency , This makes the average length of the encoded string 、 The expectation of length decreases , So as to achieve the purpose of compression .

3、 ... and 、 The protagonist of the story Lepton

Not perfect .

above JPEG Although the compression reduces the size of the image and the quality is good, it is difficult for the human eye to distinguish the differences , But because of the lossy compression , The image quality cannot be restored to the original quality , And actually at this time jpg The picture still has compressed space . Lepton You can do it in JPEG On this basis, the image is further lossless compressed .

3.1 Why choose Lepton

And lepton Similar compression tools include jpegcan,MozJPEG,PackJPG,PAQ8PX. But these tools have some defects more or less , Make it inferior to lepton More suitable for industrial production . such as PackJPG You need to rearrange all compressed pixel values in the file in the order of global sorting . This means that decompression is single threaded , At the same time, the whole image needs to be put into memory, which leads to high delay and low throughput of image processing . The picture below is lepton The comparison of several tools in the paper ：

3.2  Lepton What optimizations have been made .

First, on the algorithm Lepton Divide the image into two parts header And the image data itself ,header Use DEFLATE Do lossless compression , The image itself uses arithmetic coding instead of Holman coding for lossless compression . because JPEG Use Huffman code , This makes it difficult to use multithreading ,Lepton Use "Huffman Switch word " Improved . secondly Lepton A complex adaptive probability model is used , This model was developed by testing on a large number of field images . The goal of this model is to produce the most accurate prediction of the value of each coefficient , This produces smaller files ; Allow multithreading concurrent processing in Engineering , Allow distributed processing across multiple servers in chunks , Stream processing line by line effectively controls the memory , At the same time, it also ensures the safety of data reading and output . It is Lepton In the optimization of the above key issues , So it can be used in production environment .

3.3  Lepton stay vivo Exploration in storage

Expected earnings ：

At present, there are about 100PB data , Among them, picture data accounts for about 70%, And in the picture 90% All the pictures are jpeg Type picture , If on average 23% Compression ratio of , that 100PB * 70% * 90% * 23% = 14.5PB, Will achieve approximately 14.5PB Cost savings . At the same time, because it is lossless compression , The user experience is well guaranteed . At present lepton The design of compression function is shown in the figure below ：

Current challenges ：

1. lepton Compression and decompression require high computing performance of the server 、 High consumption .
2. Expect to make full use of idle servers CPU resources , To achieve the purpose of reducing cost and increasing efficiency .
3. It has the ability to dynamically expand and shrink capacity in the face of tidal phenomena .

The main problems faced at present ：

At present, most of the pictures are in 4M-5M, Tested for 4M-5M The file compression delay of size is 1s Left and right , The server is required at least 16 The core 、 bearing 5QPS. At this time, the utilization rate of each core is 95% above . so Lepton Compression of requires high computing performance . The current common solution is to use FPGA Card for hardware acceleration 、 And the horizontal expansion of a large number of computing nodes .FPGA The use of will increase the cost of hardware , Reduce the costs and benefits of compression .

Solution ：

In order to solve the above problems and challenges , We try to use physical servers and Kubernetes The hybrid deployment method solves the problems of the use of computing resources and dynamic expansion , The schematic diagram of the structure is as follows ：

The management of physical servers and the expansion of capacity are elastically expanded through the registration and discovery of services 、 Through this cgroup/Taskset And so on cpu Use to manage . At the same time, connect and use Kubernetes Manage as a container 、 The flexibility of the container is more suitable for this kind of computing service .

3.4 performance evaluation

Whether synchronous compression , Or asynchronous compression , Usually, we pay more attention to the delay of image reading . Reading a large number of pictures will bring great pressure to the server , The pressure mainly comes from the image decompression calculation . To improve decompression efficiency , And make full use of the company's resources , We will lepton Compressed services are distributed in cpu Idle server , According to the degree of resource idleness , free time , Make full use of the peak and valley of resources to improve computing performance . Piezometric data ： We selected image files of different sizes , Compression and decompression tests are carried out in a stand-alone environment , The test results are as follows ：

The compression ratio is kept at 22% about .

The above figure shows the time scale of file compression and decompression of different sizes , Orange is the decompression time , Blue is compression time .

The above picture shows pictures of different sizes , stay 32 Threads concurrent , Each thread processes 100 Test data of files .

Four 、  Common problems of image compression

4.1   Distinguish lossy and lossless compression by file format

4.2  Common lossless compression algorithms

5、 ... and 、 summary

Lepton Lossless compression can provide a relatively high compression ratio , At the same time, it does not affect the user's image quality and use experience 、 In the scenario of large amount of data, you will get obvious benefits .

deficiencies It requires high computing performance 、 Only support jpeg Type of picture . For performance requirements, the industry also has relatively mature solutions , As mentioned above FPGA And elastic calculation scheme . The key is to choose a reasonable plan according to the needs of the enterprise .

quote ：

1. 《The Design, Implementation, and Deployment of a System to Transparently Compress Hundreds of Petabytes of Image Files For a File-Storage Service》
2. 《 Based on deep learning JPEG Research on image cloud storage 》
3. 《JPEG-Lepton Key modules of compression technology VLSI Research on structural design 》