Zhi-Wei Gao

Video Error Concealment by Integrating Greedy Suboptimization and Kalman Filtering Techniques

This paper addresses video error concealment techniques, focusing on motion vector recovery of P- and B-frames, to improve the decoded quality of videos when bit stream data incurs transmission errors. First, we propose a dynamic programming (DP) technique to optimize the path cost in a multistage topology and evaluate the goodness of boundary matching and side smoothness of recovered macroblocks. However, due to the high computational complexity of DP, a suboptimal alternative enhanced with an adaptive Kalman filtering algorithm is adopted instead. Experiments show that by considering the side smoothness between adjacent recovered MBs, the proposed algorithm improves the reconstructed video quality by about 0.4-0.9 dB with a packet loss rate up to 15%, compared to a traditional boundary matching algorithm. In addition, subjective image inspection demonstrates the efficiency of the proposed algorithm in retaining the continuity of lines and image details

Video error concealment by using Kalman-filtering technique

This paper addresses the technique of error concealment to recover the video quality at decoders under transmission errors. We propose Kalman filtering as a post-processing technique to traditional boundary matching algorithm (BMA) which estimates the motion vector (MV) of a corrupted macroblock by using boundary pixels of the top and bottom-adjacent MBs as the reference. Due to less information from boundary pixels, MVs estimated by using BMA are mostly inaccurate. Experiments show that by proper mathematical modeling, the Kalman filter is able to filter out the inherit noise so that the recovered MVs lead to a quality improvement of 0.4 dB/spl sim/0.72 dB for our test sequences.

Motion Vector Recovery for Video Error Concealment by Using Iterative Dynamic-Programming Optimization

This paper proposes an error concealment technique for video transmission, focusing on motion vector (MV) recovery for both inter- and intra-coded frames, to improve video quality at decoder when video bit stream data incur transmission errors. The proposed algorithm considers slice (i.e., a row of macroblocks (MBs)) errors and uses DP (Dynamic Programming) optimization technique to estimate the lost MVs in a global manner, differing from the traditional Boundary Matching Algorithm (BMA) and others that recover MVs independently for individual MBs in an erroneous slice. We also propose an iterative DP process based on 8 × 8 pixels blocks to resolve finer motions (for 8 × 8, 8 × 16, and 16 × 8 pixels blocks) that will aid in the enhancement of reconstruction quality. Experiment results show that our algorithm outperforms the well-known BMA by up to 7.28 dB and the DMVE and another prior work by Qian by up to 1.0 dB at a packet loss rate of 15%. Subjective evaluation shows that our algorithm is especially promising in preserving line/curve features and motion details.

MPEG-4 video error detection by using data hiding techniques

Two major congenital limitations in wireless transmission are its narrow channel bandwidth and high noise characteristic. Hence, multimedia data are compressed to conform to the channel bandwidth, but on the other hand also suffer from less immunity from channel noise. Many researches have focused on video error resilience and error concealment techniques to improve received quality in presence of high channel noise. However, they often assumed that video errors have been correctly located. If not, error concealment can not be properly applied. In this paper, we propose using data hiding techniques to embed useful information (e.g., the types of MV-related data in MPEG-4 data-partitioned packet) at encoder so that real errors can be detected by consistency-checking after this information is extracted at decode side. This helps the application of error concealment to improve video quality. Experiments show that our proposed equally-spaced-check-point (ESCP) error detection scheme can detect 70% of bit errors that can not be detected traditionally.

Joint source-channel video coding based on the optimization of end-to-end distortions

In this research, a JSCC (Joint Source-Channel Coding) video coding system based on the optimization of end-to-end distortions is proposed. To this end, a model describing the rate and distortion of video contents in the error-prone environment is established and estimated. Based on the constructed models, the proposed system is capable of controlling coding parameters, such as channel code rate, quantization parameters, and number of packets, adaptively in accordance with the channel condition and bit rate budge, to optimize the quality of received video at decoder side. Experimental results show that our proposed JSCC system can improve PSNR by up to 5 dB, with respect to the traditional EEP (Equal error protection) technique.

Error Resilient Motion Estimation and Mode Decision for Robust H.264/AVC Video Transmission

Due to temporal prediction, via motion vectors, adopted in most video coding standards, video quality is substantially degraded at decoder side as a result of error propagation. In order to enhance robustness of existing temporal-prediction-based techniques, one prediction strategy, called error resilient motion estimation (ERME), to take both coding efficiency and error propagation into considerations is proposed in this paper. A similar concept is applied to the problem of mode decision for H.264/AVC videos, called error resilient mode decision (ERMD), so as to choose an optimal mode (intra-prediction or variable block sizes for motion estimation) for robust video transmission. Experiment results show that the integration of these two algorithms really enhances the robustness of H.264/AVC videos and outperforms traditional algorithms by 1~7 dB at a packet loss rate of 15%.

Video error concealment by using iterative dynamic-programming optimization

This paper addresses an error concealment technique, focusing on motion vector (MV) recovery for inter-coded frames, to improve video quality at decoder when video bit stream data are incurred transmission errors. The proposed algorithm considers slice (i.e., a row of macroblocks (MBs)) errors and uses DP (Dynamic Programming) optimization technique to estimate the lost MVs in a combined manner, differing from the traditional Boundary Matching Algorithm (BMA) which recovers MV independently for each erroneous MB. We also consider MV recovery for blocks of 8×8, 8×16, and 16×8 pixels (rather than 16×16 pixels MBs only) and apply an iterative DP process to refine the estimated results. Experimental results show that our algorithm outperforms the well-known BMA by up to 1.36 dB in PSNR and a newly published algorithm [8] by up to 0.56 dB at a pack loss rate of up to 15%. Subjective evaluation shows that our algorithm takes the advantages of moderate speed and accurate recovery of contiguous line/curve features in images.

Error resilient motion estimation for video coding

Due to temporal prediction adopted in most video coding standards, errors occurred in the current frame will propagate to succeeding frames that refer to it. This causes substantial degradation in reconstructed video quality at decoder side. In order to enhance robustness of existing temporal prediction techniques, another prediction strategy, called error resilient motion estimation (ERME), to take both coding efficiency and error propagation into considerations is proposed. To find MVs that satisfy the above two requirements, a constrained optimization problem with a new criterion is thus formed for ME. The proposed algorithm is implemented for H.264 video coding standard, where multiple reference frames are allowed for ME. From experimental results, the proposed algorithm can improve PSNR by up to 1.0 dB (at a packet loss rate of 20%) when compared with the full search ME with traditional SAD (sum of absolute difference) criterion.

Enhancing error resiliency for multi-hypothesis video coding techniques

In this paper, two techniques based on the optimization of end-to-end distortions in presence of channel errors, are proposed to enhance the error resiliency of the multi-hypothesis coded videos. First, an error resilient motion estimation algorithm is introduced for a given hypothesis-weighting vector to consider the error resiliency, in addition to the traditional coding efficiency. Second, based on the availability of MVs, the proposed adaptive hypothesis-weighting algorithm makes error resiliency adaptive to video contents, frame by frame. Experiment results show that both techniques are capable of improving PSNR performance by up to 1 dB when the packet loss rate is 15%.