Zhenzhong Chen

Recent advances in rate control for video coding

In this paper, we review the recent advances in rate control techniques for video coding. The rate control algorithms recommended in the video coding standards are briefly described and analyzed. Recent advances, such as new concepts in rate-distortion modelling and quality constrained control, are presented. With these techniques, the rate control performance can be improved. The paper not only summarizes these recent rate control techniques but also provides explicit directions for future rate control algorithm design.

Dynamic Bit Allocation for Multiple Video Object Coding

In MPEG-4, a visual scene may be treated as a composition of video objects and coded at object level. Such a flexible video coding framework makes it possible to code different video objects with different priority according to human perceptual characteristics. In this paper, we introduce a novel dynamic bit allocation framework to improve the subjective quality in such an object-based video coding system. We incorporate the rate distortion models with the dynamic priorities of the video objects and jointly encode video objects to minimize the weighted distortion within the bit budget constraint. We guarantee the human-interested video objects a better reconstructed quality by using the weighted bit allocation strategy in favour of the video objects with higher priority. To obtain the priority automatically, we apply a visual attention model. 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 dynamic bit allocation framework

Towards Rate-Distortion Tradeoff in Real-Time Color Video Coding

In this paper, we address to the key problem in real-time video coding, the rate-distortion tradeoff. We found that the distribution of the integer transform coefficients in H.264 color video coding can be approximated by Laplacian distribution but the rate-distortion characteristics should be modelled for the luminance and chrominance components separately. So we propose separable rate-distortion models for color video coding. The feedback from the encoder buffer is analyzed by a control-theoretic adaptation approach. To achieve smooth video quality and satisfy the delay constraints in real-time applications, a novel rate distortion tradeoff controller is employed. Simulations results demonstrate that the proposed approach can achieve smooth video quality without sacrificing overall quality while efficiently utilize the bandwidth without buffer overflow or underflow

A two-pass rate control algorithm for H.264/AVC high definition video coding

In this paper, we propose a novel two-pass rate control algorithm to achieve constant quality for H.264/AVC high definition video coding. With the first-pass collected rate and distortion information and the built model of scene complexity, the encoder can determine the expected distortion which could be achieved in the second-pass encoding under the target bit rate. According to the built linear distortion-quantizer (D-Q) model, before encoding one frame, the quantization parameter can be solved to realize constant quality encoding. After encoding one frame, the model parameters will be updated with linear regression method to ensure the prediction accuracy of the quantization parameter of next encoded frame with the same coding type. In order to obtain the expected distortion of each frame under the target bit rate, a GOP-level bit allocation scheme is also designed to adjust the target bit rate of each GOP based on the scene complexity of the GOP in the second-pass encoding. In addition, the effect of scene change on the updating of D-Q model is considered. The model will be re-initialized at the scene change to minimize modeling error. The experimental results show that compared with the latest two-pass rate control algorithm, our proposed algorithm can significantly improve the bit control accuracy at comparable coding performance in terms of constant quality and average PSNR. On average, the improvement of bit control accuracy achieved about 90%.

Joint texture-shape optimization for MPEG-4 multiple video objects

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 R-D models. The dynamic programming 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.

Distortion variation minimization in real-time video coding

In this paper, we review the rate distortion tradeoff issues in real-time video coding and introduce a minimum variation (MINVAR) distortion criterion based approach. 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. The proposed approach aims to achieve a smooth decoded picture quality for pleasing human visual experience. The performance of the proposed method is evaluated with H.264. The experimental results demonstrate that using the proposed approach, the decoded picture quality is smoother than the traditional H.264 joint model (JM) rate control without sacrificing global quality such that a better subjective visual quality is guaranteed.

Optimal bit allocation for MPEG-4 multiple video objects

In this paper, we present an optimal bit allocation algorithm for multiple video objects (MVOs) in MPEG-4 video coding. We combine the rate-distortion models for texture and shape information to achieve the accurate rate control. To optimize the bit allocation for multiple video objects, we use the dynamic programming (DP). The simulation results demonstrate that the proposed algorithm outperforms the MPEG-4 verification model not only in terms of buffer stability but the objective quality.

Improved single video object rate control for MPEG-4

Rate control plays a central role in constant bit-rate (CBR) video coding applications using MPEG-4. The paper considers single video object (SVO) rate control for MPEG-4 and presents a new rate control algorithm based on the quadratic rate-distortion model. Based on a new measure for the encoding complexity, the constraints of the model parameter estimation and a novel quantizer control strategy are proposed. Simulation results show that the MPEG-4 coder, using the proposed algorithm, can achieve a higher PSNR than a coder using the conventional rate control algorithm.

Constant distortion rate control for H.264/AVC high definition videos with scene change

In this paper, we propose a constant distortion rate control algorithm for H.264/AVC high definition videos with scene change. With the rate and distortion information of each frame, the frame scene complexity is modeled and the scene change is detected in the first pass encoding. In the second-pass encoding, the expected distortion of each GOP under the constraint of target bits is derived based on the first-pass statistics. And the quantization step (Q-step) of each frame can be obtained with a linear distortion-quantizer (D-Q) model by considering the parameter update of this model at scene change. The experimental results show that our proposed algorithm can provide more constant quality among different video scenes, compared to traditional joint model (JM) rate control algorithm.

A Rate and Distortion Analysis of Multiscale Binary Shape Coding Based on Statistical Learning

In this paper, we propose a statistical learning-based approach to analyze the rate-distortion characteristics of MPEG-4 multiscale binary shape coding. We employ the polynomial kernel function and epsiv-insensitive loss function for our support vector regression. To improve the accuracy of the estimation, rate and distortion related features are incorporated in the statistical learning framework. Our experimental results show that the proposed approach can achieve good performance, e.g., modelling the rate-distortion curves accurately.