Blocking artifacts are introduced in many block-based coding systems, and its reduction can significantly improve the subjective quality of compressed video. The H.264/AVC uses an in-loop deblocking filter to remove the blocking artifacts. The filter considers some coding conditions in its adaptive deblocking filtering such as coded block pattern (CBP), motion vector, macroblock type, etc. for inter-predicted blocks, however, it does not consider much for intra-coded blocks. In this paper, we utilize the human visual system (HVS) characteristic and the local characteristic of image blocks to modify the boundary strength (BS) of the intra-deblocking filter in order to gain improvement in the subjective quality and also to reduce the complexity in filtering intra coded slices. In addition, we propose a low-complexity deblocking method which utilizes the correlation between vertical and horizontal boundaries of a block in inter coded slices. Experimental results show that our proposed method achieves not only significant gain in the subjective quality but also some PSNR gain, and reduces the computational complexity of the deblocking filter by 36.23% on average.

Applying Distributed Video Coding (DVC) to mobile devices that have limited computation and power resources can be a very challenging problem due to its high-complexity decoding. To address this, this paper proposes a DVC bitstream organizer. The proposed DVC bitstream organizer reduces the complexity associated with repetitive channel decoding and SI generation in a flexible manner. It allows users to choose a means of minimizing the computational complexity of the DVC decoder according to their preferences and the device's resource limitations. An experiment shows that the proposed method increases decoding speeds by up to 25 times.

Matching pursuit is a signal expansion technique whose efficiency for motion compensated residual image has been successfully demonstrated in the MPEG-4 development. However, one of the practical concerns related to applying matching pursuit algorithm to real-time coding of video is its massive computation required for finding atoms. This paper proposes a new fast method based on three properties of basis functions used in the signal expansion. The first one is the symmetry property of the 1-D bases. The second one is that one can preclude many bases that cannot be atom by checking a simple mathematical condition. The last one is the classification property of 2-D bases in a given dictionary. Experimental result shows that our method can perform the same matching pursuit without any image degradation using only about 40% of computational load required by the conventional fast method based on separability of 2-D Gabor dictionary. Furthermore, if negligible quality degradation is allowed, the method can be extended to perform matching pursuit with only about 10% of the computational load required by the conventional fast method. We apply the proposed fast matching pursuit method to scalable coding of video with two layers.