Treffer: Content-Based Texture Analysis and Synthesis for Low Bit-Rate Video Coding Using Perceptual Models

Determining perceptually irrelevant and redundant information from human point of view is one of the fundamental problems today that is limiting the performance of current video compression algorithms. The performance of the existing video compression standards is based on minimizing the cumulative sum of objective distortion, namely mean squared error (MSE), measured for each pixel. Recently there have been quite a few advancements made to understand human visual models and apply them for a compact representation at very low bitrates. However, most of these approaches offer advantages over a very limited range of input sequences using predefined models for analysis of static scene, human head, and human body. The existing video compression standards typically aim to increase the spectral flatness measure of the residue signal, by increasing the number of both spatial and temporal predictors. With the increase in the choices of predictors, the corresponding bits, required to convey the choice of the predictor to the decoder, also increases. This mandates the need for jointly optimizing the distortion and the required side information for a given quantization factor using special rate distortion measures. This thesis is aimed at suggesting alternative solution of removing perceptual redundancy without increasing the number of predictors using two approaches. The first one is to increase the spectral flatness measure by removing perceptually irrelevant residual information. The second one is to model the perceptually relevant residual information loss due to quantization and parameterize the same for synthesizing it at the decoder end. This basically evolves around two analytical and estimation problems. The first problem is to identify the perceptually irrelevant quantization noise and remove it from the resulting source. The second problem is to model the perceptually relevant quantization noise. The first contribution of this dissertation is to classify regions into homogenous / non-homogenous and rigid / ...