Adaptive GOF residual operation algorithm in video compression

Abstract

Residual operations are introduced in many video compression algorithms to exploit temporal redundancy in video sequences. One of the most effective and computationally simple algorithms is 3D-DWT-SPIHT algorithm. Nevertheless, it only utilizes intra-GOF temporal redundancy. In order to eliminate inter-GOF redundancy, the Simple GOF Residual Operation (SGRO) algorithm was introduced, where residual operations are performed every two GOFs. However, when background mutations occur, the PSNR of reconstructed target GOF significantly drops; on the other hand, when video sequences are temporally stationary, SGRO algorithm fails to fully utilize inter-GOF temporal redundancy due to its imperative insertion of no-residual-operation GOFs. In this paper, we propose a new Adaptive GOF Residual Operation (AGRO) algorithm, based upon a criterion on residual operations, which is derived from an empirical formula of image complexity established by us. AGRO algorithm always selects the best residual operation manner according to the contents of video sequences: by detecting contents of video sequences, it cancels residual operations where background mutations happen, while encouraging residual operations where video sequences are temporally stationary. Therefore, AGRO algorithm prevents the significant drop in compression effects resulted from background mutations, and in the meantime, fully utilizes inter-GOF temporal redundancy. In addition, AGRO algorithm demonstrates an innate error-propagation-resistant property. Numerical results show that AGRO algorithm renders a significant PSNR increase over SGRO algorithm whenever background mutations occur or temporally stationary sequences dominate.