Rate, Distortion and Classification tradeoff

Abstract

The purpose of the Master thesis is to improve the classification performance upon reception of an image, for a given rate and distortion. It builds on the work of Blau and Michaeli [BM18] [BM19] to integrate perceptual quality in coding by introducing the divergence between input and output signal distributions as a criterion, thereby redefining the rate/distortion tradeo↵ to include perception. My research extends this by incorporating image gradient statistics for enhanced segmentation in compressed images, therefore resulting in a slight modification of the Rate/Distortion/Perception model for improved classification performance. This modification is based on the use of a 2D Haar transform, which allows to include the divergence between the high frequency components of the original and reconstructed images in the criterion to be optimized. Central to my approach is the use of Machine Learning, especially Wasserstein Generative Adversarial Networks (WGANs), marking a significant integration of traditional coding techniques with contemporary AI innovations.