Learning-Based Conditional Image Coder Using Color Separation

TL;DR

This paper introduces a neural network-based image codec that separates color components for parallel processing, reducing complexity and memory usage while maintaining high compression quality.

Contribution

The proposed Conditional Color Separation (CCS) codec enables parallel processing of color components, reducing computational complexity and memory usage compared to traditional neural network codecs.

Findings

Over 40% less memory usage

2x faster encoding speed

22% faster decoding speed

Abstract

Recently, image compression codecs based on Neural Networks(NN) outperformed the state-of-art classic ones such as BPG, an image format based on HEVC intra. However, the typical NN codec has high complexity, and it has limited options for parallel data processing. In this work, we propose a conditional separation principle that aims to improve parallelization and lower the computational requirements of an NN codec. We present a Conditional Color Separation (CCS) codec which follows this principle. The color components of an image are split into primary and non-primary ones. The processing of each component is done separately, by jointly trained networks. Our approach allows parallel processing of each component, flexibility to select different channel numbers, and an overall complexity reduction. The CCS codec uses over 40% less memory, has 2x faster encoding and 22% faster decoding speed, with only 4% BD-rate loss in RGB PSNR compared to our baseline model over BPG.