| dc.description.abstract | With the rapid development of technology, People are always persistent in pursuing the high quality of video. Therefore, multimedia devices like monitors, players that have high resolution started rapidly increasing in numbers. In order to compress the significant increasing of data storage effectively, HEVC utilize multiple techniques to efficiently decrease bitrate. In inter-pridection, for the better effects, we proposed SVM-based fast inter CU ( Coding Units) depth decision algorithm and SVM-based fast inter PU mode decision algorithm to reduce the computational complexity. In SVM-based fast inter CU depth decision algorithm, we can skip certain depth by using SVM with features, including motion vector variance, CBF of merge mode, neighboring CU depth to classify a CTU into depth 0, depth 0~1, depth 0~2 and depth 0~3. In SVM-based fast inter PU mode decision algorithm, we use SVM with features, including motion vector variance, skip flag, the information of neighboring RDO to classify whether do early termination at 2N×2N. Besides it, we also combine CNN model with SVM in In-Loop filter of HEVC. CNN is a more and more popular technique wich can help us not only to recognize images or objects but enhance performance of portrait recently. So we can use the models to deal with the reconstruct images and thence enhance the quality of pictures. With the similar natures of blocks which SVM classes with, blocks in the same groups are trained together. Consequently, we get the models with different effects for distinct groups respectively and due to the relationship between the groups and the models, we can get the better performance than the results obtained by only using CNN without SVM. Finally, we combine two algorithms and CNN to compare with HEVC. Furthermore, in intra-prediction, by applying SVM with features consist of the CUs’ information and space relation, it can develop the criterion of early CU splitting and termination so that we can speed up intra-prediction by classifying a CTU into depth 0~2, depth 1~3. Again, we also use the classifications to train CNN model, and introduce it in deblocking filter on purpose to enhance the image performance. We improve effect on intra-prediction as well as inter-prediction, and both they can get eminent achievement. Our experiment results that the method surpasses mode (HM) with BD-PSNR (0.36 dB), BD-BR (-6.2%) on intra-prediction and BD-PSNR (0.25 dB), BD-BR (-6.2%) on inter-prediction which can even get 6% time saving compared to HM16.0. | en_US |