International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Image Compression by Visual Pattern Vector Quantization (VPVQ)
DCC '08 Proceedings of the Data Compression Conference
Design of steerable filters for feature detection using canny-like criteria
IEEE Transactions on Pattern Analysis and Machine Intelligence
Overview of the Scalable Video Coding Extension of the H.264/AVC Standard
IEEE Transactions on Circuits and Systems for Video Technology
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This paper proposes a novel fractional compensation approach for spatial scalable video coding. It simultaneously exploits inter layer correlation and intra layer correlation by learning-based mapping. Instead of using an enhancement layer reconstruction as an entire reference, a set of reference pairs are generated from high-frequency components of both base layer and enhancement layer reconstructions at previous frame. The reference set, which consists of low-resolution and high-resolution patches, can be generated in both encoder and decoder by on-line learning. During the encoding of enhancement layer, a prediction is first gotten from base layer, from which low-resolution patches are extracted. These patches are then used as indices to find the matched high-resolution patches from the reference set. Finally, the prediction enhanced by the high-resolution patches is used for coding. The proposed approach does not need any motion bits. With our proposed FC approach, the performance of H.264 SVC can be improved up to 2.4dB in spatial scalable coding.