Video encoding and transcoding using machine learning
Proceedings of the 9th International Workshop on Multimedia Data Mining: held in conjunction with the ACM SIGKDD 2008
Requantization transcoding for H.264/AVC video coding
Image Communication
Variable and constant bitrate in a DVC to H.264/AVC transcoder
Image Communication
An approach for an AVC to SVC transcoder with temporal scalability
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
Fast mode decision algorithm for H.264/AVC-to-SVC transcoding with temporal scalability
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Hi-index | 0.00 |
Recent developments have given birth to H.264/AVC: a video coding standard offering better bandwidth to video quality ratios than previous standards (such as H.263, MPEG-2, MPEG-4, etc.), due to its improved inter- and intraprediction modes at the expense of higher computation complexity. It is expected that H.264/AVC will take over the digital video market, replacing the use of previous standards in most digital video applications. This creates an important need for heterogeneous video transcoding technologies from older standards to H.264. In this paper, we focus our attention on the interframe prediction, the most computationally intensive task involved in the heterogeneous video transcoding process. This paper presents a novel macroblock (MB) mode decision algorithm for interframe prediction based on data mining techniques to be used as part of a very low complexity heterogeneous video transcoder. The proposed approach is based on the hypothesis that MB coding mode decisions in H.264 video have a correlation with the distribution of the motion compensated residual in the decoded video. We use data mining tools to exploit the correlation and derive decision trees to classify the incoming decoded MBs into one of the several coding modes in H.264. The proposed approach reduces the H.264 MB mode computation process into a decision tree lookup with very low complexity. For general validation purposes, we apply our algorithm to two of the most important heterogeneous video transcoders: MPEG-2 to H.264 and H.263 to H.264. Our results show that the our data-mining based transcoding algorithm is able to maintain a good video quality while considerably reducing the computational complexity by 72% on average when applied in MPEG-2 to H.264 transcoders, and by 62% on average when applied in H.263 to H.264 transcoders. Finally, we conduct a comparative study with some of the most prominent fast interprediction methods for H.264 presented in the literature. Our results show t- hat the proposed data mining-based approach achieves the best results for video transcoding applications.