A Prediction Error Compression Method with Tensor-PCA in Video Coding

Abstract

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.