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29 October 2014 Applications of just-noticeable depth difference model in joint multiview video plus depth coding
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A new multiview just-noticeable-depth-difference(MJNDD) Model is presented and applied to compress the joint multiview video plus depth. Many video coding algorithms remove spatial and temporal redundancies and statistical redundancies but they are not capable of removing the perceptual redundancies. Since the final receptor of video is the human eyes, we can remove the perception redundancy to gain higher compression efficiency according to the properties of human visual system (HVS). Traditional just-noticeable-distortion (JND) model in pixel domain contains luminance contrast and spatial-temporal masking effects, which describes the perception redundancy quantitatively. Whereas HVS is very sensitive to depth information, a new multiview-just-noticeable-depth-difference(MJNDD) model is proposed by combining traditional JND model with just-noticeable-depth-difference (JNDD) model. The texture video is divided into background and foreground areas using depth information. Then different JND threshold values are assigned to these two parts. Later the MJNDD model is utilized to encode the texture video on JMVC. When encoding the depth video, JNDD model is applied to remove the block artifacts and protect the edges. Then we use VSRS3.5 (View Synthesis Reference Software) to generate the intermediate views. Experimental results show that our model can endure more noise and the compression efficiency is improved by 25.29 percent at average and by 54.06 percent at most compared to JMVC while maintaining the subject quality. Hence it can gain high compress ratio and low bit rate.
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Chao Liu, Ping An, Yifan Zuo, and Zhaoyang Zhang "Applications of just-noticeable depth difference model in joint multiview video plus depth coding", Proc. SPIE 9273, Optoelectronic Imaging and Multimedia Technology III, 92732R (29 October 2014);

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