论文标题
逐个像素的学习像素无损图像压缩法具有59K参数和平行解码
A Learned Pixel-by-Pixel Lossless Image Compression Method with 59K Parameters and Parallel Decoding
论文作者
论文摘要
本文考虑了无损图像压缩，并提出了一个学习的压缩系统，该系统可以实现最新的无损压缩性能，但仅使用59K参数，该参数比最近在文献中提出的其他学到的系统少30倍以上。探索系统基于学习的像素逐像素无损图像压缩方法，其中每个像素的概率分布参数是通过使用一个简单的神经网络包含59K参数来处理像素的因果关系邻域（即先前编码/解码的像素）来获得的。这种因果关系导致解码器顺序运行，即必须对每个像素进行依次评估神经网络，这使用常见的GPU软件和硬件大大增加了解码时间。为了减少解码时间，提出并实施并行解码算法。将所获得的无损图像压缩系统与文献中的传统和学到的系统进行了比较，以压缩性能，编码编码时间和计算复杂性。
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.
