Efficient Techniques for Depth Video Compression Using Weighted Mode Filtering

Abstract

Abstract-This paper proposes efficient techniques to compress a depth video by taking into account coding artifacts, spatial resolution, and dynamic range of the depth data. Due to abrupt signal changes on object boundaries, a depth video compressed by conventional video coding standards often introduces serious coding artifacts over object boundaries, which severely affect the quality of a synthesized view. We suppress the coding artifacts by proposing an efficient post-processing method based on a weighted mode filtering and utilizing it as an in-loop filter. In addition, the proposed filter is also tailored to reconstruct efficiently the depth video from the reduced spatial resolution and the low dynamic range. The down/upsampling coding approaches for the spatial resolution and the dynamic range are used together with the proposed filter in order to further reduce the bit rate. We verify the proposed techniques by applying to an efficient compression of multiview-plus-depth data, which has emerged as an efficient data representation for 3D video. Experimental results show that the proposed techniques significantly reduce the bit rate while achieving a better quality of the synthesized view in terms of both objective and subjective measures. IndexTerms-Weighted mode filtering, depth down/upsampling, depth dynamic range, depth coding, 3D video

Image bit-depth enhancement via maximum-a-posteriori estimation of graph AC component

“…Our work is a significant improvement over existing bit-depth enhancement works in that we are the first in the literature to define signal smoothness formally using GSP tools for the bit-depth enhancement problem, and propose a computation-efficient MAP algorithm that produces good approximate solutions minimizing the expected distortion. Our algorithm can also be used for broader applications that require bitdepth enhancement: e.g., 3D surface refinement by enhancing the bit-precision of depth maps [12]; compression schemes that encode an image at shallower bit-depth than captured for bitrate saving, and then recover the least significant bits (LSB) at decoder [13].…”

Joint processing and fast encoding algorithm for multi-view depth video

“…Consequently, the ideal compression performance cannot be achieved, even if the state-of-the-art encoding methods are used. To improve encoding and virtual view rendering performance, many depth video processing algorithms [17][18][19][20][21][22][23] have been proposed. Hu et al [17] proposed a depth video restoration algorithm which is effective for depth video corrupted by additive white Gaussian noise.…”

“…Hu et al [17] proposed a depth video restoration algorithm which is effective for depth video corrupted by additive white Gaussian noise. Nguyen et al [18] suppressed the coding artifacts over object boundaries by using a weighted mode filtering. Zhao et al [19] proposed a depth no-synthesis-error (D-NOSE) model and presented a smoothing scheme for depth video coding.…”

“…In our previous works, the correlation of depth video is enhanced for high compression performance [22,23]. The main contribution of these algorithms [17][18][19][20][21][22][23] is for better virtual view quality or high compression ratio under H.264/AVC framework. In 3D-HEVC, the size of CU and prediction modes are different from those of H.264/ AVC; ideal compression ratio might not be achieved under 3D-HEVC.…”

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

“…In essence, DIBR shifts the pixel of the texture video at the original view to the correct position in the synthesized view based on depth data. Thus, the quality of the synthesized view can be affected by the qualities of both texture and depth videos [3]. While errors in texture videos affect the interpolation values, depth errors result in wrong geometric displacements of pixels in the rendered view.…”