Digital Image Processing
Compressed Video Communications
Compressed Video Communications
GPCA: an efficient dimension reduction scheme for image compression and retrieval
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A Multi-Scale Hybrid Linear Model for Lossy Image Representation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Generalized Principal Component Analysis (GPCA)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video Compression and Retrieval of Moving Object Location Applied to Surveillance
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Fast covariance computation and dimensionality reduction for sub-window features in images
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
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Discrete Cosine Transform (DCT), which is employed by block-based hybrid video coding to encode motion prediction errors, has dominated practical video coding standards for several decades. However, DCT is only a good approximation to Principle Component Analysis (PCA, also called KLT), which is optimal among all unitary transformations. PCA is rejected by coding standards due to its complexity. This paper tries to use a matrix form of PCA (which we call tensor-PCA) to encode prediction errors in video coding. This method retains the performance of traditional PCA, but can be computed with much less time and space complexity. We compared tensor-PCA with DCT and GPCA in motion prediction error coding, which shows that it is a good trade-off between compression efficiency and computational cost.