Spatial intra-prediction based on mixtures of sparse representations

Abstract-In this paper, we consider the problem of spatial prediction based on sparse representations. Several algorithms dealing with this problem can be found in the literature. We propose a novel method involving a mixture of sparse representations. We first place this approach into a probabilistic framework and then derive a practical procedure to solve it. Comparisons of the rate-distortion performance show the superiority of the proposed algorithm with regard to other stateof-the-art algorithms

Learning Clustering-Based Linear Mappings for Quantization Noise Removal

International audienceThis paper describes a novel scheme to reduce the quantization noise of compressed videos and improve the overall coding performances. The proposed scheme first consists in clustering noisy patches of the compressed sequence. Then, at the encoder side, linear mappings are learned for each cluster between the noisy patches and the corresponding source patches. The linear mappings are then transmitted to the decoder where they can be applied to perform de-noising. The method has been tested with the HEVC standard, leading to a bitrate saving of up to 9.63%

Inter-prediction methods based on linear embedding for video compression

International audienceThis paper considers the problem of temporal prediction for inter-frame coding of video sequences using locally linear embedding (LLE). LLE-based prediction, first considered for intra-frame prediction, computes the predictor as a linear combination of K nearest neighbors (K-NN) searched within one or several reference frames. The paper explores different K-NN search strategies in the context of temporal prediction, leading to several temporal predictor variants. The proposed methods are tested as extra inter-frame prediction modes in an H.264 codec, but the proposed concepts are still valid in HEVC. The results show that significant rate-distortion performance gains are obtained with respect to H.264 (up to 15.31% bit-rate saving)

Sparse Approximation With Adaptive Dictionary For Image Prediction

International audienceNo abstrac