Livrable D3.3 of the PERSEE project : 2D coding tools

49Livrable D3.3 du projet ANR PERSEECe rapport a été réalisé dans le cadre du projet ANR PERSEE (n° ANR-09-BLAN-0170). Exactement il correspond au livrable D3.3 du projet. Son titre : 2D coding tool

Region-Based Template Matching Prediction for Intra Coding

Copy prediction is a renowned category of prediction techniques in video coding where the current block is predicted by copying the samples from a similar block that is present somewhere in the already decoded stream of samples. Motion-compensated prediction, intra block copy, template matching prediction etc. are examples. While the displacement information of the similar block is transmitted to the decoder in the bit-stream in the first two approaches, it is derived at the decoder in the last one by repeating the same search algorithm which was carried out at the encoder. Region-based template matching is a recently developed prediction algorithm that is an advanced form of standard template matching. In this method, the reference area is partitioned into multiple regions and the region to be searched for the similar block(s) is conveyed to the decoder in the bit-stream. Further, its final prediction signal is a linear combination of already decoded similar blocks from the given region. It was demonstrated in previous publications that region-based template matching is capable of achieving coding efficiency improvements for intra as well as inter-picture coding with considerably less decoder complexity than conventional template matching. In this paper, a theoretical justification for region-based template matching prediction subject to experimental data is presented. Additionally, the test results of the aforementioned method on the latest H.266/Versatile Video Coding (VVC) test model (version VTM-14.0) yield an average Bjøntegaard-Delta (BD) bit-rate savings of −0.75% using all intra (AI) configuration with 130% encoder run-time and 104% decoder run-time for a particular parameter selection

Inter-frame Prediction with Fast Weighted Low-rank Matrix Approximation

In the field of video coding, inter-frame prediction plays an important role in improving compression efficiency. The improved efficiency is achieved by finding predictors for video blocks such that the residual data can be close to zero as much as possible. For recent video coding standards, motion vectors are required for a decoder to locate the predictors during video reconstruction. Block matching algorithms are usually utilized in the stage of motion estimation to find such motion vectors. For decoder-side motion derivation, proper templates are defined and template matching algorithms are used to produce a predictor for each block such that the overhead of embedding coded motion vectors in bit-stream can be avoided. However, the conventional criteria of either block matching or template matching algorithms may lead to the generation of worse predictors. To enhance coding efficiency, a fast weighted low-rank matrix approximation approach to deriving decoder-side motion vectors for inter frame video coding is proposed in this paper. The proposed method first finds the dominating block candidates and their corresponding importance factors. Then, finding a predictor for each block is treated as a weighted low-rank matrix approximation problem, which is solved by the proposed column-repetition approach. Together with mode decision, the coder can switch to a better mode between the motion compensation by using either block matching or the proposed template matching scheme

Fast intra prediction in the transform domain

In this paper, we present a fast intra prediction method based on separating the transformed coefficients. The prediction block can be obtained from the transformed and quantized neighboring block generating minimum distortion for each DC and AC coefficients independently. Two prediction methods are proposed, one is full block search prediction (FBSP) and the other is edge based distance prediction (EBDP), that find the best matched transformed coefficients on additional neighboring blocks. Experimental results show that the use of transform coefficients greatly enhances the efficiency of intra prediction whilst keeping complexity low compared to H.264/AVC

Continuous Action Recognition Based on Sequence Alignment

Continuous action recognition is more challenging than isolated recognition because classification and segmentation must be simultaneously carried out. We build on the well known dynamic time warping (DTW) framework and devise a novel visual alignment technique, namely dynamic frame warping (DFW), which performs isolated recognition based on per-frame representation of videos, and on aligning a test sequence with a model sequence. Moreover, we propose two extensions which enable to perform recognition concomitant with segmentation, namely one-pass DFW and two-pass DFW. These two methods have their roots in the domain of continuous recognition of speech and, to the best of our knowledge, their extension to continuous visual action recognition has been overlooked. We test and illustrate the proposed techniques with a recently released dataset (RAVEL) and with two public-domain datasets widely used in action recognition (Hollywood-1 and Hollywood-2). We also compare the performances of the proposed isolated and continuous recognition algorithms with several recently published methods