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.

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 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.

Conventional correlation methods are efficient considerations for block-based stereo matching. However, the process is fallible in scenarios where a rotational offset is present between a stereo image pair. A method is proposed in this paper to resolve this problem. Since the proposed method conforms to block-based operations, it is most appropriate for time critical applications. Experimental analysis validated that the proposed method yields more precise stereo results in comparison to conventional correlation. This enhancement promotes the effectiveness of correlation-based stereo algorithms for better quality in stereo perception.

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%.

We propose an error resilience transcoder based on the intra refresh technique for H.264/AVC videos. Our algorithm focuses on making decisions on the number of inserted intra MBs, allocations of proper MBs for intra refresh, and the quantization parameter for each P-frame, according to varying channel condition and frame contents. We build source distortion, channel distortion, and bit-rate models for prediction and adopt the Lagrangian multiplier approach for constrained optimization. Existing algorithms are compared in experiments. The results show that our algorithm improves PSNR by 0.28~2.76 dB, compared to the "equal intra refresh" approach. Our algorithm also outperforms CBERC (Chen and Wang, 2001) by 0.1~1.13 dB, with the same number of inserted intra MBs, but different MB priorities in allocation