A Practical Approach for Rate-Distortion-Perception Analysis in Learned Image Compression

TL;DR

This paper introduces a practical method to analyze the trade-off between rate, distortion, and perception in learned image compression by fixing the rate, enabling more meaningful perceptual evaluations beyond traditional metrics like PSNR.

Contribution

It proposes a novel approach to fix the rate in learned image compression, facilitating systematic analysis of the rate-distortion-perception trade-off.

Findings

Provides insights into perceptual quality at fixed rates.

Enables principled evaluation of learned image compression methods.

Highlights limitations of traditional metrics like PSNR.

Abstract

Rate-distortion optimization (RDO) of codecs, where distortion is quantified by the mean-square error, has been a standard practice in image/video compression over the years. RDO serves well for optimization of codec performance for evaluation of the results in terms of PSNR. However, it is well known that the PSNR does not correlate well with perceptual evaluation of images; hence, RDO is not well suited for perceptual optimization of codecs. Recently, rate-distortion-perception trade-off has been formalized by taking the Kullback-Leibner (KL) divergence between the distributions of the original and reconstructed images as a perception measure. Learned image compression methods that simultaneously optimize rate, mean-square loss, VGG loss, and an adversarial loss were proposed. Yet, there exists no easy approach to fix the rate, distortion or perception at a desired level in a practical learned image compression solution to perform an analysis of the trade-off between rate, distortion and perception measures. In this paper, we propose a practical approach to fix the rate to carry out perception-distortion analysis at a fixed rate in order to perform perceptual evaluation of image compression results in a principled manner. Experimental results provide several insights for practical rate-distortion-perception analysis in learned image compression.