Content Scalability in Multiple Description Image and Video Coding

High compression ratio, scalability and reliability are the main issues for transmitting multimedia content over best effort networks. Scalable image and video coding meets the user requirements by truncating the scalable bitstream at different quality, resolution and frame rate. However, the performance of scalable coding deteriorates rapidly over packet networks if the base layer packets are lost during transmission. Multiple description coding (MDC) has emerged as an effective source coding technique for robust image and video transmission over lossy networks. In this research problem of incorporating scalability in MDC for robust image and video transmission over best effort network is addressed. The first contribution of this thesis is to propose a strategy for generating more than two descriptions using multiple description scalar quantizer (MDSQ) with an objective to jointly decoded any number of descriptions in balanced and unbalanced manner. The distortion constraints and design conditions for multichannel unbalanced description coding (MUDC) using several MDSQs for improving quality as the number of description is increased are formulated. Secondly, the design of MDSQ is extended to incorporate the quality scalability in each de- scription by using the concept of successive refinement in the side quantizers of multiple description scalar quantizer called MDSQ-SR. The design conditions of the MDSQ-SR are formulated with the objective to improve the quality of side and joint decoding for any combination of quality refinement layers. The joint decoding of different spatial resolution descriptions having different quality re- finement layers is demonstrated for images by combining MUDC and MDSQ-SR schemes respectively. Finally, a fully scalable multiple description video coding (SMDVC) scheme is proposed by integrating MUDC and MDSQ-SR schemes in a motion compensated temporal filtering based video coding framework. The pro- posed SMDVC scheme is capable of generating and joint decoding any number of descriptions in balanced and unbalanced manner at any quality, resolution and frame rate. According to the experimental results the unbalanced joint decoding results into 1.1 dB better peak to signal noise ratio (PSNR) than the balanced joint decod- ing at the same data rate. Furthermore, the joint decoding of MDSQ-SR based scheme gives an average of 1.35 dB and 0.3 dB better PSNR performance with re- spect to the state-of-the-art embedded-MDSQ for images and video respectively. The PSNR performance of the MDSQ-SR based video scheme is improved by 0.2-0.6 dB by controlling inter description and motion vector redundancies. In addition to superior rate-distortion performance than embedded-MDSQ, MDSQ- SR has reduced the computational complexity by 83%.