Robust Motion Vector Coding and Error Concealment in MCTF-Based Video Coding

In the latest video coding frameworks, efficiency of motion vector (MV) coding is becoming increasingly important because of the growing bit rate portion of motion information. However, neither the conventional median predictor, nor the newer schemes such as the minimum bit rate prediction scheme and the hybrid scheme, can effectively eliminate the local redundancy of motion vectors. In this paper, we present the prioritized reference decision scheme for efficient motion vector coding, based on the H.264/AVC framework. This scheme makes use of a boolean indicator to specify whether the median predictor is to be used for the current MV or not. If not, the median prediction is considered not suitable for the current MV, and this information is used for refining the possible space of a group of reference MVs including 4 neighboring MVs and the zero MV. This group of MVs is organized to be a prioritized list so that the reference MV with highest priority is to be selected as the prediction value. Furthermore, the boolean indicators are coded into the modified code words of mb type and sub mb type, so as to reduce the overhead. By applying the proposed scheme, the structure and the applicability problems with the state-of-theart MBP scheme have been overcome. Experimental result shows that the proposed scheme achieves a considerable reduction of bits for MVDs, compared with the conventional median prediction algorithm. It also achieves a better and much stabler performance than MBP-based MV coding.

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Robust Motion Vector Coding and Error Concealment in MCTF-Based Video Coding

Cited by 3 publications

References 6 publications

Prioritized reference decision for efficient motion vector coding

Prioritized reference decision for efficient motion vector coding

A hybrid motion-vector coding scheme based on an estimation of the locality for motion-vector difference

An Efficient Motion Vector Coding Scheme Based on Prioritized Reference Decision

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