ADAPTIVE SCALABLE VIDEO CODING

This thesis is composed of three main parts which include three contributions in slightly different fields, all lying on the same framework: Adaptive Scalable Video Coding. First part is about integration of motion compensated temporal filtering (MCTF), the basis for temporal scalability in scalable video coding methods, to the latest non-scalable video compression standard, i.e., H.264/AVC. We propose a GOP structure to implement block-based adaptive MCTF within the H.264/AVC syntax using stored B-pictures, similar to the motion-compensated 5/3 wavelet filtering. We provide experimental results to compare the results of our proposed codec with those of other scalable wavelet video coders which use MCTF. The proposed scheme is also integrated into H.264/AVC reference software as ‘Hierarchical B pictures’ or ‘Temporal Pyramid’ and it is currently under investigation of MPEG Core Experiments for the upcoming Scalable Video Coding standard (SVC). Secondly, we worked on content adaptive scalability type selection problem. State of the art scalable video coders provide different options, such as temporal, spatial and SNR scalability, where bitrate reduction using each scalability type results in different kinds and/or levels of visual distortion depending on the content and the bitrate. In most cases, a single scalability type does not fit the whole video well, and scaling option selection can be optimized for each temporal segment depending on the content of that segment and the target bitrate. This dependency between selection of scalability type, video content, and bitrate is not well investigated in the literature. In this work, assuming that the video is temporally segmented by some content analysis scheme, we propose a method to choose the best scaling type for each temporal segment that results in minimum visual distortion among temporal, spatial and SNR scalability for fully embedded scalable video coders. We employ an objective distortion measure that consists of a linear combination of four component measures, which are a flatness measure, a blockiness measure, a blurriness measure, and a temporal jerkiness measure, to quantify artifacts caused by bitrate reduction by spatial size reduction, frame rate reduction, and quantization parameter scaling. Coefficients of the linear combination are adapted to temporal segment (shot) content type by a training procedure. We then define the best scaling option for each shot as the one with the minimum objective distortion score. Two subjective tests have been performed to validate the proposed procedure for shot-based selection of optimal scalability type on soccer videos. Soccer videos whose bitrate are reduced from 600 kbps to 100-300 kbps by the proposed contentadaptive selection of scalability type have been deemed visually superior to those whose bitrates are reduced by a single scalability option for the entire test sequence. Finally, we worked on adaptive peer-to-peer (P2P) streaming using scalable multiple description coding. Efficient P2P video streaming is a challenging task due to time-varying nature of both the number of available peers and network/channel conditions. To this effect, we propose i) a new flexible scalable multiple description coding (MDC) method, where the number of descriptions, and the rate and redundancy level of each description can be adapted on the fly (by postprocessing of a fully-embedded scalable coded bitstream), and ii) a new adaptive TCP Friendly Rate Controlled (TFRC) P2P streaming system based on this new MDC scheme. The optimization of the design parameters of the proposed MDC scheme according to network conditions is discussed within the context of the proposed adaptive P2P streaming framework, where the number and quality of available streaming peers/paths are a priori unknown and vary in time. Experimental results, by means of NS-2 network simulation of a P2P video streaming system, show that adaptation of the number and rate of descriptions/layers and the redundancy level of each description according to network conditions yields significantly superior performance when compared to other scalable MDC schemes using a fixed number of descriptions/layers with fixed rate and redundancy level.