C4.5: programs for machine learning
C4.5: programs for machine learning
VideoQ: an automated content based video search system using visual cues
MULTIMEDIA '97 Proceedings of the fifth ACM international conference on Multimedia
Machine learning of event segmentation for news on demand
Communications of the ACM
C4.5 competence map: a phase transition-inspired approach
ICML '04 Proceedings of the twenty-first international conference on Machine learning
The H.264 Video Coding Standard
IEEE MultiMedia
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
The VC-1 Video Coding Standard
IEEE MultiMedia
Heterogeneous video transcoding to lower spatio-temporalresolutions and different encoding formats
IEEE Transactions on Multimedia
Low-Complexity Heterogeneous Video Transcoding Using Data Mining
IEEE Transactions on Multimedia
Exploiting the directional features in MPEG-2 for H.264 intra transcoding
IEEE Transactions on Consumer Electronics
Image classification for content-based indexing
IEEE Transactions on Image Processing
A Fast MB Mode Decision Algorithm for MPEG-2 to H.264 P-Frame Transcoding
IEEE Transactions on Circuits and Systems for Video Technology
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Machine learning has been widely used in video analysis and search applications. In this paper, we describe a non-traditional use of machine learning in video processing - video encoding and transcoding. Video encoding and transcoding are computationally intensive processes and this complexity is increasing significantly with new compression standards such as H.264. Video encoders and transcoders have to manage the quality vs. complexity tradeoff carefully. Optimal encoding is prohibitively complex and sub-optimal coding decisions are usually used to reduce complexity but also sacrifices quality. Resource constrained devices cannot use all the advanced coding tools offered by the standards due to computational needs. We show that machine learning can be used to reduce the computational complexity of video coding and transcoding problems without significant loss in quality. We have developed the use of machine learning in video coding and transcoding and have evaluated it on several encoding and transcoding problems. We describe the general ideas in the application of machine learning and present the details of four different problems: 1) MPEG-2 to H.264 video transcoding, 2) H.263 to VP6 transcoding, 3) H.264 encoding and 4) Distributed Video Coding (DVC). Our results show that use of machine learning significantly reduces the complexity of encoders/transcoders and enables efficient video encoding on resource constrained devices such as mobile devices and video sensors.