21,749 research outputs found
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
Learned Quality Enhancement via Multi-Frame Priors for HEVC Compliant Low-Delay Applications
Networked video applications, e.g., video conferencing, often suffer from
poor visual quality due to unexpected network fluctuation and limited
bandwidth. In this paper, we have developed a Quality Enhancement Network
(QENet) to reduce the video compression artifacts, leveraging the spatial and
temporal priors generated by respective multi-scale convolutions spatially and
warped temporal predictions in a recurrent fashion temporally. We have
integrated this QENet as a standard-alone post-processing subsystem to the High
Efficiency Video Coding (HEVC) compliant decoder. Experimental results show
that our QENet demonstrates the state-of-the-art performance against default
in-loop filters in HEVC and other deep learning based methods with noticeable
objective gains in Peak-Signal-to-Noise Ratio (PSNR) and subjective gains
visually
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