Paper Info
Title
H.264/Advanced Video Control Perceptual Optimization Coding Based on JND-Directed Coefficient Suppression
Abstract
The field of video coding has been exploring the compact representation of video data, where perceptual redundancies in addition to signal redundancies are removed for higher compression. Many research efforts have been dedicated to modeling the human visual system's characteristics. The resulting models have been integrated into video coding frameworks in different ways. Among them, coding enhancements with the just noticeable distortion (JND) model have drawn much attention in recent years due to its significant gains. A common application of the JND model is the adjustment of quantization by a multiplying factor corresponding to the JND threshold. In this paper, we propose an alternative perceptual video coding method to improve upon the current H.264/advanced video control (AVC) framework based on an independent JND-directed suppression tool. This new tool is capable of finely tuning the quantization using a JND-normalized error model. To make full use of this new rate distortion adjustment component the Lagrange multiplier for rate distortion optimization is derived in terms of the equivalent distortion. Because the H.264/AVC integer discrete cosine transform (DCT) is different from classic DCT, on which state-of-the-art JND models are computed, we analytically derive a JND mapping formula between the integer DCT domain and the classic DCT domain which permits us to reuse the JND models in a more natural way. In addition, the JND threshold can be refined by adopting a saliency algorithm in the coding framework and we reduce the complexity of the JND computation by reusing the motion estimation of the encoder. Another benefit of the proposed scheme is that it remains fully compliant with the existing H.264/AVC standard. Subjective experimental results show that significant bit saving can be obtained using our method while maintaining a similar visual quality to the traditional H.264/AVC coded video.
Year
DOI
Venue
2013
10.1109/TCSVT.2013.2240919
IEEE Transactions on Circuits and Systems for Video Technology
Keywords
Field
DocType
coefficient suppression,h264/avc,perceptual,video coding,quantization,visualization,encoding
Computer vision,Coding tree unit,Context-adaptive variable-length coding,Human visual system model,Computer science,Artificial intelligence,Motion estimation,Quantization (signal processing),Rate–distortion optimization,Scalable Video Coding,Context-adaptive binary arithmetic coding
Journal
Volume
Issue
ISSN
23
6
1051-8215
Citations 
PageRank 
References 
11
0.60
6
Authors
4
Name
Order
Citations
PageRank
Zhengyi Luo1357.85
Li Song232365.87
Shibao Zheng321430.64
Nam Ling461275.02