Reducing the complexity of a multiview H.264/AVC and HEVC hybrid architecture

With the advent of 3D displays, an efficient encoder is required to compress the video information needed by them. Moreover, for gradual market acceptance of this new technology, it is advisable to offer backward compatibility with existing devices. Thus, a multiview H.264/Advance Video Coding (AVC) and High Efficiency Video Coding (HEVC) hybrid architecture was proposed in the standardization process of HEVC. However, it requires long encoding times due to the use of HEVC. With the aim of tackling this problem, this paper presents an algorithm that reduces the complexity of this hybrid architecture by reducing the encoding complexity of the HEVC views. By using Na < ve-Bayes classifiers, the proposed technique exploits the information gathered in the encoding of the H.264/AVC view to make decisions on the splitting of coding units in HEVC side views. Given the novelty of the proposal, the only similar work found in the literature is an unoptimized version of the algorithm presented here. Experimental results show that the proposed algorithm can achieve a good tradeoff between coding efficiency and complexity

MIXED-RESOLUTION HEVC BASED MULTIVIEW VIDEO CODEC

Studies have shown that mixed resolution based video codecs, also known as asymmetric spatial inter/intra view video codecs are successful in efficiently coding videos for low bitrate trans-mission. In this paper a HEVC based spatial resolution scaling type of mixed resolution coding model for frame interleaved multiview videos is presented. The proposed codec is designed such that the information in intermediate frames of the center and neighboring views are down-sampled, while the frames still retaining the original size. The codec’s reference frames structure is designed to efficiently encode frame interleaved multi-view videos using a HEVC based mixed resolution codec. The multi-view test video sequences were coded using the proposed codec and the standard MV-HEVC. Results show that the pro-posed codec gives significantly higher coding performance over the MV- HEVC codec at low bitrates

HEVC based Mixed-resolution Stereo Video Coding for Low Bitrate Transmission

This paper presents a mixed resolution stereo video coding model for High Efficiency Video Codec (HEVC). The challenging aspects of mixed resolution video coding are enabling the codec to encode frames with different frame resolution/size and using decoded pictures having different frame resolution/size for referencing. These challenges are further enlarged when implemented using HEVC, since the incoming video frames are subdivided into coding tree units. The ingenuity of the proposed codec’s design, is that the information in intermediate frames are down-sampled and yet the frames can retain the original resolution. To enable random access to full resolution decoded frame in the decoded picture buffer as reference frame a downsampled version of the decoded full resolution frame is used. The test video sequences were coded using the proposed codec and standard MV-HEVC. Results show that the proposed codec gives a significantly higher coding performance over the MV- HEVC codec

Joint Reconstruction of Multi-view Compressed Images

The distributed representation of correlated multi-view images is an important problem that arise in vision sensor networks. This paper concentrates on the joint reconstruction problem where the distributively compressed correlated images are jointly decoded in order to improve the reconstruction quality of all the compressed images. We consider a scenario where the images captured at different viewpoints are encoded independently using common coding solutions (e.g., JPEG, H.264 intra) with a balanced rate distribution among different cameras. A central decoder first estimates the underlying correlation model from the independently compressed images which will be used for the joint signal recovery. The joint reconstruction is then cast as a constrained convex optimization problem that reconstructs total-variation (TV) smooth images that comply with the estimated correlation model. At the same time, we add constraints that force the reconstructed images to be consistent with their compressed versions. We show by experiments that the proposed joint reconstruction scheme outperforms independent reconstruction in terms of image quality, for a given target bit rate. In addition, the decoding performance of our proposed algorithm compares advantageously to state-of-the-art distributed coding schemes based on disparity learning and on the DISCOVER