In this paper, we present a novel adaptive frequency coefficient suppression technique (AFCS) for region-of-interest (ROI) based H.264/AVC video coding. With the consideration of human visual system (HVS), AFCS can adaptively suppress the selective frequency coefficients of 4 x 4 blocks in non-ROI. Many bits for non-ROI are thus saved and reallocated to ROI to improve its visual quality. In addition, unlike most of the existing ROI-based methods based on the MB-layer rate control method, any frame-layer rate control method can be adopted in our scheme, which reduces the computational overhead of the encoder.

High Efficiency Video Coding (HEVC) is an ongoing standard, and it employs the quad-tree block partitioning structure which includes coding unit, prediction unit, and transform unit. This content-adaptive coding tree structure can improve HEVC coding efficiency significantly, but it also consumes large computational complexity. This paper proposed a fast intra coding unit size decision algorithm to reduce the heavy complexity of HEVC encoding. First, the proposed algorithm reduced unit sizes search by using the classifier, which is based on the statistical learning. Second, an early largest unit size decision was designed to skip the checking of unnecessary unit sizes. As compared to the full search algorithm in HEVC reference software, experimental results show that the proposed algorithm achieves 50.4% computation saving on average with 1.83% bit rate increase and 0.070dB peak signal-to-noise ratio loss.

In multi-view video plus depth (MVD) coding based free viewpoint video applications, a few reference viewpoints’ texture and depth videos should be compressed and transmitted at the server side. At the terminal side, the display view videos could be the decoded reference view videos or the virtual viewpoints’ videos which are synthesized by DIBR technology. The entire video quality of all display views are decided by the number of reference viewpoints and the compression distortion of each reference viewpoint’s texture and depth videos. This paper studies the impact of the reference viewpoints selection on the entire video quality of all display views. The results show that depending on the available network bandwidth, the MVD coding requires different selections of reference viewpoints to maximize the entire video quality of all display views.