Rate control and constant quality rate control for MPEG video compression and transcoding

The focus of this thesis is the design of rate-control (RC) algorithms for constant quality (CQ) video encoding and transcoding, where CQ is measured by the variance of quality in PSNR (peak signal-to-noise ratio). By modeling DCT coefficients as having Laplacian distributions, Laplacian rate/models are developed for MPEG-4 encoding and transcoding. These models accurately estimate the rate and distortion (in PSNR) of MPEG-4 compressed bitstreams. The rate model is applied to a CBR (constant bit rate) encoding algorithm. This algorithm offers a better or similar PSNR as compared to the Q2 [7] algorithm with a lower variation in bitrate. Thus, it outperforms Q2. These models are then applied to CQ video coding and transcoding. Most CBR control algorithms aim to produce a bitstream that meets a certain bitrate with the highest quality. Due to the non-stationary nature of video sequences, the quality of the compressed sequence changes over time, which is not desirable to end-users. To provide a solution to this problem, six CQ encoding algorithms are proposed: the first two are VBR (variable bit rate) algorithms with a fixed target quality (FTQ), the next two are CBR algorithms with FTQ, and the last two are CBR algorithms with a dynamic target quality (DTQ). Within each group of two, the quality is controlled either at the frame level (using the Laplacian rate/distortion model) or at the macroblock level (using the actual distortions). With the success of these algorithms, the CQ DTQ encoding algorithms are extended to MPEG-4 video transcoding (bitrate reduction with requantization). These CQ transcoding algorithms can handle the problems that are uniquely present in transcoders, such as the lack of the original sequence and requantization. Similar to their encoding counterparts, these CQ transcoding algorithms have an extra degree of freedom to balance the quality variation with the accuracy to the target bitrate and the average quality. Simulation results indicate that these algorithms offer lower PSNR variance while having similar/lower average PSNR and bitrate when compared with Q2T and TM5T (transcoding version of Q2 and TM5). Besides proposing MPEG-4 CQ RC algorithms, an MPEG-2 rate-control algorithm is also developed based on TM5. It aims at improving the subjective quality measured by using Watson's DVQ (digital video quality) metric. When compared with TM5, it provides a better DVQ. However, since Watson's DVQ metric is not a standard way to estimate the subjective quality, PSNR is still used in the rest of the researc

An efficient rate control algorithm for a wavelet video codec

Rate control plays an essential role in video coding and transmission to provide the best video quality at the receiver's end given the constraint of certain network conditions. In this paper, a rate control algorithm using the Quality Factor (QF) optimization method is proposed for the wavelet-based video codec and implemented on an open source Dirac video encoder. A mathematical model which we call Rate-QF (R - QF) model is derived to generate the optimum QF for the current coding frame according to the target bitrate. The proposed algorithm is a complete one pass process and does not require complex mathematical calculation. The process of calculating the QF is quite simple and further calculation is not required for each coded frame. The experimental results show that the proposed algorithm can control the bitrate precisely (within 1% of target bitrate in average). Moreover, the variation of bitrate over each Group of Pictures (GOPs) is lower than that of H.264. This is an advantage in preventing the buffer overflow and underflow for real-time multimedia data streaming

Regularity scalable image coding based on wavelet singularity detection

In this paper, we propose an adaptive algorithm for scalable wavelet image coding, which is based on the general feature, the regularity, of images. In pattern recognition or computer vision, regularity of images is estimated from the oriented wavelet coefficients and quantified by the Lipschitz exponents. To estimate the Lipschitz exponents, evaluating the interscale evolution of the wavelet transform modulus sum (WTMS) over the directional cone of influence was proven to be a better approach than tracing the wavelet transform modulus maxima (WTMM). This is because the irregular sampling nature of the WTMM complicates the reconstruction process. Moreover, examples were found to show that the WTMM representation cannot uniquely characterize a signal. It implies that the reconstruction of signal from its WTMM may not be consistently stable. Furthermore, the WTMM approach requires much more computational effort. Therefore, we use the WTMS approach to estimate the regularity of images from the separable wavelet transformed coefficients. Since we do not concern about the localization issue, we allow the decimation to occur when we evaluate the interscale evolution. After the regularity is estimated, this information is utilized in our proposed adaptive regularity scalable wavelet image coding algorithm. This algorithm can be simply embedded into any wavelet image coders, so it is compatible with the existing scalable coding techniques, such as the resolution scalable and signal-to-noise ratio (SNR) scalable coding techniques, without changing the bitstream format, but provides more scalable levels with higher peak signal-to-noise ratios (PSNRs) and lower bit rates. In comparison to the other feature-based wavelet scalable coding algorithms, the proposed algorithm outperforms them in terms of visual perception, computational complexity and coding efficienc

Adaptive temporal decimation algorithm with dynamic time window

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