Texture processing for image/video coding and super-resolution applications

Unrestricted Textured image/video processing for coding and super-resolution is investigated in this thesis to improve coding efficiency and prediction accuracy. The research consists of three main parts.; First, a synthesis-based texture coding technique that uses low-quality video as the side information to control the output texture for video coding is proposed. As compared with the current pure synthesis algorithm, the proposed algorithm is generic, in the sense that the behavior and quality of the output texture can be adjusted by the amount of the side information and determined by the user. We develop an area-adaptive side information assignment technique to improve coding efficiency by given bit-budget. Additionally, we also provide the texture decomposition algorithm to maximize the synthesis performance by decomposing the non-synthesizable illumination component from the input video. Simulations demonstrate the performance of the proposed technique.; Second, the addition of a new component to the traditional synthesis-based texture video coding algorithm is investigated in this chapter. That is, we add the side information in form of low-quality video to enhance the texture video synthesis performance with reducing the unpleasant mismatch between analyzed and synthesized regions. As compared with the conventional synthesis algorithm, our algorithm is more flexible since the behavior and quality of the output texture can be adjusted by the amount of the side information, which is determined by the user. To this end, we develop an area-adaptive side information selection scheme that chooses the proper amount of the side information for a given bit budget. Furthermore, we propose a texture decomposition scheme that extracts the non-synthesizable illumination component from the source video for separate coding so as to maximize the synthesis functionality. The superior performance of the proposed texture video synthesis technique is demonstrated by several coding examples.; Third, a texture interpolation technique based on the locally piecewise auto-regressive (PAR) model and the non-local (NL) training procedure is investigated. The proposed PAR/NL scheme selects model parameters adaptively based on local image properties with an objective to improve the interpolation performance of non-adaptive models,e.g., the bicubic algorithm. To determine model parameters for stochastic texture, we use the non-local (NL) learning algorithm to update and refine these local model parameters under the assumption that the PAR model parameters are self-regular. As compared to previous interpolation algorithms, the proposed PAR/NL scheme boosts texture details, and eliminates blurring artifacts perceptually. Experimental results are given to demonstrate the performance of the proposed technique.