Scalable Video Coding

With the rapid improvements in digital communication technologies, distributing high-definition visual information has become more widespread. However, the available technologies were not sufficient to support the rising demand for high-definition video. This situation is further complicated when the network resources such as the available bandwidth fluctuates, or packet losses occur during transmission. In this dissertation we present several video compression techniques which are capable of adapting with the varying network conditions. We address both challenges namely, the fluctuations in the available resources such as the bandwidth and processing power, and packet losses. These problems in turn translates into degradation of the perceived video playback as jitter, and delay before video playback starts. Hence, we concentrate on developing robust and fast adaptive video coding schemes necessary for handling the changes in the physical characteristics of the communication networks. We present a new multi-layer scalable video coding (SVC) method for optimizing the bit-per-pixel rate of the video which is robust against packet losses. The method reduces the quality degradation in presence of data loss by re-organizing the frames in a hierarchical structure and improving the video quality through decomposing each frame suitably to restrict the error propagation. Moreover, we present a solution for the quality degradation in video reconstruction when the video is scrambled for privacy protection. We also present two methods based on multiple description video coding (MDC) to handle packet losses in networks with a high rate of transmission error. The proposed methods are based on combining SVC with MDC through decomposing the video into spatial sub-streams in the first method, and SNR sub-streams in the second method. In both proposed methods, the error resilience of the video is increased. The proposed methods have the capability of being used as SVC methods where any data loss or corruption reduces the quality of the video in a minimized way, and except for the case when all descriptions are lost, the video streams do not experience jitter at playback. The proposed methods provide the feasibility of reducing data rate by scaling down the video whenever the connection suffers from a low bandwidth problem. We also propose Discrete Wavelet Transform (DWT)-based optimizations for MDC. A major drawback in MDC methods is their inefficiency in terms of bit-per-pixel which is a consequence of preserving correlation between decomposed video segments. We propose a method based on the self-similarity between DWT coefficients at different frequency levels to improve the coding efficiency of DWT-based MDC. In the proposed method, whenever a description is lost the coefficients at the delivered descriptions are utilized for estimating the missing data using self-similarity property.