Joint source video coding : joint rate control for H.264/AVC video coding

In video broadcast systems, the principal objective is to obtain the best possible visual quality at a specific rate constraint and channel settings. Traditional methods for broadcasting multiple digital video bitstreams over a single-channel use a straightforward approach: they divide the existing capacity of a broadcasting channel equally among all programmes, and then they independently encode each video programme at a constant bit rate (CBR). In this dissertation, we focus on how to design efficient rate control schemes for the joint coding of video bitstreams using H.264/AVC video encoders in such a way that the sum of all the bit rates meets the negotiated connection parameters. A possible solution is to encode each video bitstream using VBR video encoding algorithms, guaranteeing that the permissible range of variation of the bit rate of each source is restricted by at least the capacity of the channel. In order to estimate the impact in terms of video quality of encoded video bitstreams, several issues have been studied: the criteria to allocate bandwidth between the different video programmes and how visual quality is assessed. The way video quality is measured is vital for designing video broadcast systems that potentially result in the degradation of the visual quality. Such metrics have the potential to allow designers to optimise video broadcast systems to deliver higher quality while decreasing system costs. For decades, the mean squared error (MSE) has been the dominant objective video quality metric. Nevertheless, on many occasions the MSE exhibits weak performance and has been extensively criticized for serious shortcomings, mainly when dealing with perceptually significant signals. Research on how perceptual video quality metrics, metrics that incorporate human visual perception, can be used in the coding process is rather important. In the present dissertation, two video quality metrics that use the structural properties of human vision were selected, the Structural SIMilarity (SSIM) index and the JND (Just Noticeable Distortion). A novel approach to a joint video source coding, based on perceptual distortion, is thus proposed. The H.264/AVC standard specifies the decoding process and the bit-stream syntaxes accordingly allowing research towards the optimization of the encoding process concerning coding performance improvement and complexity reduction. Rate-distortion (R-D) based methods are frequently used to enhance and smooth video quality. However the lack of R-D models for joint video encoders, using perceptual video quality metrics, confines their application to joint video coding. To provide a more accurate estimation, the rate distortion vi Joint Source Video Coding relation based on the characteristics of the H.264/AVC coding to support the best video quality perception was researched and modeled. One important outcome is that the models deliver good performance in terms of quality prediction accuracy and strong correlation with subjective ratings. So, one can exploit the accuracy of quality prediction of existing perceptual video quality metrics to facilitate joint coding decisions. These results demonstrate that subjective metrics can be incorporated in joint source coding systems resulting in a perceived quality improvement. Experiments have been conducted to validate the performance of the proposed algorithms. Objective and subjective video quality results show that the proposed algorithms outperform current state-of-the-art methods in the simulations. With suitable bit rate allocation, the fluctuation of objective video quality is reduced in a joint video coding process. In summary, the main contributions of this thesis are as follows: modelling of R-D of H.264/AVC using perceptual means to assess the distortion; development of joint source coding algorithms for controlling a statistical multiplexing process of different streams into a fixed bandwidth channel that incorporate perceptual information; study how joint coding results correlate with subjective quality assessment.