A Learned Pixel-by-Pixel Lossless Image Compression Method with 59K Parameters and Parallel Decoding

Abstract

This paper considers lossless image compression and presents a learned compression system that can achieve state-of-the-art lossless compression performance but uses only 59K parameters, which is more than 30x less than other learned systems proposed recently in the literature. The explored system is based on a learned pixel-by-pixel lossless image compression method, where each pixel’s probability distribution parameters are obtained by processing the pixel’s causal neighborhood (i.e. previously encoded/decoded pixels) with a simple neural network comprising 59K parameters. This causality causes the decoder to operate sequentially, i.e. the neural network has to be evaluated for each pixel sequentially, which increases decoding time significantly with common GPU software and hardware. To reduce the decoding time, parallel decoding algorithms are proposed and implemented. The obtained lossless image compression system is compared to traditional and learned systems in the literature in terms of compression performance, encoding-decoding times and computational complexity.

Requirements

pytorch
PIL
easydict
numpy
torch-vision
torchnet

Training

For training:

1. Edit your json file.
2. In your json file, set "mode" as train.
3. Set "train_data" as the directory of training data.

Real Compression

For real compression:

1. In your json file, set "mode" as test.
2. Set "run_mode" as encode.
3. Set "run_type" as "S" (sequential) or "P" for (parallel).
4. Set "decoder_method" as 1 (Wavefront parallel decoding method) or 2 (diagonal parallel decoding method).
5. Set "test_data" as the directory of test images.

Decoding

For decoding operation:

1. In your json file, set "mode" as test.
2. Set "run_mode" as decode.
3. Set "run_type" as "S" (sequential) or "P" for (parallel).
4. Set "bitstream_path" as the directory of decoded bitsream.
5. Set "H" and "W" as the height and width of the image to be decoded.
6. For parallel decoding, set "decoder_method" as 1 (Wavefront parallel decoding method) or 2 (diagonal parallel decoding method).

How to run code

To run the code with adjusted settings:

python main.py configs/your_json_file.json

Arithmetic Coding

In our work, we used NumpyAc (Fast Autoregressive Arithmetic Coding) which is avaliable at github link:

https://github.com/zb12138/NumpyAc