Improving view rendering quality and coding efficiency by suppressing compression artifacts in depth-image coding

Abstract

We consider the effect of depth-image compression artifacts on the quality of virtual views rendered using neighboring views. Such view rendering processes are utilized in new video applications such as 3D television (3DTV) and free viewpoint video (FVV). We first analyze how compression artifacts in compressed depth-images result in distortions in rendered views. We show that the rendering position error is a monotonic function of the coding error. For the scenario in which cameras are arranged with parallel optical axes , we further demonstrate specific properties of rendering position error. Exploiting special characteristics of depth-images, namely smooth regions separated by sharp edges, we investigate a possible solution to suppress compression artifacts by encoding depth-images with a recently published sparsity-based in-loop de-artifacting filter. Simulation results show that applying such techniques not only provides significantly higher coding efficiency for depth-image coding, but, more importantly, also improves the quality of rendered views in terms of PSNR and subjective quality.

Model-based complexity-aware coding for multiview video plus depth

“…It has been shown in [12] there exists a linear relationship between the depth map distortion ∆L, and the translational rendering position error ∆P in the synthesized view as ∆P (x, y) = δx(x, y) δy(x, y)…”

Rate-Distortion Optimization for depth map coding with distortion estimation of synthesized view

“…The circle dots in green are the original pixels in the reference texture and the circle ones in red are pixels in the synthesized view texture after warping with per-pixel disparity values. The relationship between depth value and disparity value D can be written as (2) [15]:…”

Efficient Techniques for Depth Video Compression Using Weighted Mode Filtering

“…Note that the "bell" width of the spreading function G r is controlled by the filter parameter σ r . To reduce the complexity, when the localized histogram is computed using (13), only the d th bin satisfying |d − D(q)| ≤ B should be updated for each neighboring pixel q, where the threshold B is determined to meet the condition G r (B/2) = 0.3. As mentioned in Section II, the width (↔ σ r ) is determined by the amount of noise in the input data f (p).…”

“…Kim et al proposed a new distortion metric that considers camera parameters and global video characteristics, and then used the metric in the rate-distortion optimized mode selection to quantify the effects of depth video compression on the synthesized view quality [12]. Lai et al showed that a rendering error in the synthesized view is a monotonic function of the coding error, and presented a method to suppress compression artifacts using a sparsity-based de-artifacting filter [13]. Some approaches have been proposed to encode a downsampled depth map and to use a special upsampling filter after decoding to recover the depth edge information [14]- [16].…”