Reduction of Computational Complexity for HEVC Inter Prediction with Support Vector Machine

• Main Authors: Jia-Kai Liu, 劉家凱
• Other Authors: Yin-yi Lin
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/42u5f7

碩士 === 國立中央大學 === 通訊工程學系 === 107 === With the advancement of technology and high requirement, multimedia devices that have high resolution started to rapidly increase in numbers. In order to compress the significant increasing of data storage effectively, HEVC utilize multiple techniques to efficiently decrease bitrate。Hence, in this thesis, we proposed SVM-based fast inter CU 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. At last, we combine two algorithm to compare with HEVC, the average BDBR rises by less than 0.1% and 30% encoding time saving.