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In the emerging next generation video coding standard Versatile Video Coding (VVC) developed by the Joint Video Exploration Team (JVET), sub-block-based inter-prediction plays a key role in promising coding tools such as Affine Motion Compensation (AMC) and sub-block-based Temporal Motion Vector Prediction (sbTMVP). With sub-block-based inter-prediction, a coding block is divided into sub-blocks, and the motion information of each sub-block is derived individually. Although sub-block-based inter-prediction can provide a higher quality prediction benefiting from a finer motion granularity, it still suffers two problems: uneven prediction quality and boundary discontinuity. In this paper, we present a method of interweaved prediction to further improve sub-block-based inter-prediction. With interweaved prediction, a coding block with AMC or sbTMVP mode is divided into sub-blocks with two different dividing patterns, so that a corner position of a sub-block in one dividing pattern coincides with the central position of a sub-block in the other dividing pattern. Then two auxiliary predictions are generated by AMC or sbTMVP with the two dividing patterns, independently. The final prediction is calculated as a weighted-sum of the two auxiliary predictions. Theoretical analysis and statistical data prove that interweaved prediction can significantly mitigate the two problems in sub-block-based inter-prediction. Simulation results show that the proposed methods can achieve 0.64% BD-rate saving on average with the random access configurations. On sequences with rich affine motions, the average BD-rate saving can be up to 2.54%. [ABSTRACT FROM AUTHOR]

This paper proposes a deep learning method for intra prediction. Different from traditional methods utilizing some fixed rules, we propose using a fully connected network to learn an end-to-end mapping from neighboring reconstructed pixels to the current block. In the proposed method, the network is fed by multiple reference lines. Compared with traditional single line-based methods, more contextual information of the current block is utilized. For this reason, the proposed network has the potential to generate better prediction. In addition, the proposed network has good generalization ability on different bitrate settings. The model trained from a specified bitrate setting also works well on other bitrate settings. Experimental results demonstrate the effectiveness of the proposed method. When compared with high efficiency video coding reference software HM-16.9, our network can achieve an average of 3.4% bitrate saving. In particular, the average result of 4K sequences is 4.5% bitrate saving, where the maximum one is 7.4%. [ABSTRACT FROM AUTHOR]

Computers are developing along with a new trend from the dual-core and quad-core processors to ones with tens or even hundreds of cores. Multimedia, as one of the most important applications in computers, has an urgent need to design parallel coding algorithms for compression. Taking intraframe/image coding as a start point, this paper proposes a pure line-by-line coding scheme (LBLC) to meet the need. In LBLC, an input image is processed line by line sequentially, and each line is divided into small fixed-length segments. The compression of all segments from prediction to entropy coding is completely independent and concurrent at many cores. Results on a general-purpose computer show that our scheme can get a 13.9 times speedup with 15 cores at the encoder and a 10.3 times speedup at the decoder. Ideally, such near-linear speeding relation with the number of cores can be kept for more than 100 cores. In addition to the high parallelism, the proposed scheme can perform comparatively or even better than the H.264 high profile above middle bit rates. At near-lossless coding, it outperforms H.264 more than 10 dB. At lossless coding, up to 14% bit-rate reduction is observed compared with H.264 lossless coding at the high 4:4:4 profile. [ABSTRACT FROM PUBLISHER]

While directional adaption is introduced into traditional transforms, different orders of two 1-D transforms will result in different results of one 2-D transform. Based upon an anisotropic image model, this paper analyzes the effect of transform orders in terms of theoretical coding gain. Our results reveal that the transform orders have little effect on the coding gain with full decomposition, good directional modes and good interpolation. However, in practical compression schemes, since high-pass bands are not decomposed fully because of the consideration on complexity, different transform orders have different coding performances, which can be solved by an adaptive transform order. Motivated by our analyzed results, a directional filtering transform (dFT, in order to distinguish from the common usage on DFT) is proposed in this paper to better exploit correlations among samples in H.264 intraframe coding. It provides an evenly distributed set of prediction modes with an adaptive transform order. Both interblock and intrablock correlations are exploited in this scheme. Experimental results in H.264 intraframe coding demonstrate its superiority both objectively and subjectively. [ABSTRACT FROM PUBLISHER]