In this paper, we propose a statistical learning based approach to analyze the rate-distortion characteristics
of multiscale binary shape coding. We employ the polynomial kernel function and incorporate rate-distortion
related features for our support vector regression. ε-Insensitive loss function is chosen to improve the estimation
robustness. The parameter tuning is also studied. Moreover, we discuss the feature selection which helps to
improve the estimation accuracy. Comparing to the traditional method, our proposed framework provides
better rate distortion estimation not only on simple shapes but also on complex shapes.
MPEG-4 supports object-level video coding. It is a challenge to design an optimal bit allocation strategy which considers not only how to distribute bits among multiple video objects (MVO's) but also how to achieve optimization between the texture and shape information. In this paper, we present a uniform framework for optimal multiple video object bit allocation in MPEG-4. We combine the rate-distortion (R-D) models for the texture and shape information of arbitrarily shaped video objects to develop the joint texture-shape rate-distortion models. The dynamic programming (DP) technique is applied to optimize the bit allocation for the multiple video objects. The simulation results demonstrate that the proposed joint texture-shape optimization algorithm outperforms the MPEG-4 verification model on the decoded picture quality.
In this paper, we propose a minimum variation (MINVAR) distortion criterion based approach for the rate distortion tradeoff in video coding. The MINVAR based rate distortion tradeoff framework provides a local optimization strategy as a rate control mechanism in real time video coding applications by minimizing the distortion variation while the corresponding bit rate fluctuation is limited by utilizing the encoder buffer. We use the H.264 video codec to evaluate the performance of the proposed method. As shown in the simulation results, the decoded picture quality of the proposed approach is smoother than that of the traditional H.264 joint model (JM) rate control algorithm. The global video quality, the average PSNR, is maintained while a better subjective visual quality is guaranteed.
MPEG-4 treats a scene as a composition of several objects or so-called video object planes (VOPs) that are separately encoded and decoded. Such a flexible video coding framework makes it possible to code different video object with different distortion scale. It is necessary to analyze the priority of the video objects according to its semantic importance, intrinsic properties and psycho-visual characteristics such that the bit budget can be distributed properly to video objects to improve the perceptual quality of the compressed video. This paper aims to provide an automatic video object priority definition method based on object-level visual attention model and further propose an optimization framework for video object bit allocation. One significant contribution of this work is that the human visual system characteristics are incorporated into the video coding optimization process. Another advantage is that the priority of the video object can be obtained automatically instead of fixing weighting factors before encoding or relying on the user interactivity. To evaluate the performance of the proposed approach, we compare it with traditional verification model bit allocation and the optimal multiple video object bit allocation algorithms. Comparing with traditional bit allocation algorithms, the objective quality of the object with higher priority is significantly improved under this framework. These results demonstrate the usefulness of this unsupervised subjective quality lifting framework.
Video streaming over wireless channel is challenging due to a number of factors such as limited bandwidth and loss sensitivity. In this paper, we develop a novel rate control algorithm for MPEG-4 video coding. Unlike traditional rate control schemes, we jointly consider the encoding complexity variation and buffer variation as well as human visual properties to optimize the rate control efficiency. We also analyze the sensitivity of a macroblock (MB) as a result of bit errors and calculate its error sensitivity metric. This metric is used in unequal error protection (UEP) of the MB. Simulation results show that proposed approach can improve the decoded picture quality in wireless video coding and transmission.