Guillaume Laroche

Competition based prediction for skip mode motion vector using macroblock classification for the H.264 JM KTA software

H.264/MPEG4-AVC achieves higher compression gain in comparison to its predecessors H.263 and MPEG4 part 2. This gain partly results from the improvement of motion compensation tools especially the variable block size, the 1/4-pel motion accuracy and the access to multiple reference frames. A particular mode among all Inter modes is the Skip mode. For this mode, no information is transmitted except the signaling of the mode itself. In our previous work we have proposed a competing framework for better motion vector prediction and coding, also including the Skip mode. This proposal has recently been adopted by the Video Coding Expert Group (VCEG) in the Key Technical Area-software (KTA) of H.264, which is the starting point for future ITU standardization activities. In this paper we propose an extension of this method based on the adaptation of two families of predictors for the Skip mode according to the video content and to statistical criteria. A systematic gain upon the previous method, with an average of 8.2% of bits saved compared to H.264 standard, is reported.

Intra Coding With Prediction Mode Information Inference

In a typical competition-based coding, the pertinence of a prediction mode does not only depend on its own efficiency but also on the fact that it is complementary with the other modes. The method proposed in this paper to improve the intra coding of the H.264/AVC standard relies on this remark; it shows how the cost of signaling predictors that are quite similar can be avoided. Indeed, at low bitrates, the information related to the predictor signaling in intra coding reaches up to 25% of the total bitrate for the whole set of standard VCEG test sequences. In order to reduce this cost, a method reproducible at the decoder side is proposed to eliminate some predictors from the intra predictor set. The proposed method exploits the proximity of the predictors in the transform domain in order to obtain a representative and non-redundant set of predictors.