Depth Video Coding Using Adaptive Geometry Based Intra Prediction for 3-D Video Systems

Kang, Min-Koo; Ho, Yo‐Sung

doi:10.1109/tmm.2011.2169238

Cited by 29 publications

(16 citation statements)

References 11 publications

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“…The authors in [9,10] have modeled edge discontinuities using piecewiselinear approximations while triangular meshes were adopted instead in [11]. More recently, the authors in [12] have presented an adaptive geometric based Intra predictor. Nonetheless, these approximations do not adequately model depth discontinuities and produce flattened surfaces which cause annoying rendering artifacts.…”

Section: Introductionmentioning

confidence: 99%

Efficient Depth Image Compression Using Accurate Depth Discontinuity Detection and Prediction

Farrugia

2012

2012 Eighth International Conference on Signal Image Technology and Internet Based Systems

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This paper presents a novel depth image compression algorithm for both 3D Television (3DTV) and Free Viewpoint Television (FVTV) services. The proposed scheme adopts the K-means clustering algorithm to segment the depth image into K segments. The resulting segmented image is losslessly compressed and transmitted to the decoder. The depth image is then compressed using a bi-modal block encoder, where the smooth blocks are predicted using direct spatial prediction. On the other hand, blocks containing depth discontinuities are approximated using a novel depth discontinuity predictor. The residual information is then compressed using a lossy compression strategy and transmitted to the receiver. Simulation results indicate that the proposed scheme outperforms the state of the art spatial video coding systems available today such as JPEG and H.264/AVC Intra. Moreover, the proposed scheme manages to outperform specialized depth image compression algorithms such as the one proposed by Zanuttigh and Cortelazzo.

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Section: Introductionmentioning

confidence: 99%

Efficient Depth Image Compression Using Accurate Depth Discontinuity Detection and Prediction

Farrugia

2012

2012 Eighth International Conference on Signal Image Technology and Internet Based Systems

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“…Two of the most efficient techniques were published in [26] and [27]. The precision of the boundary information was seen as an important factor which needs to be improved in [28]. Piecewise linear approximations were used for selecting the important contours in [29], while encoding the contours was realized by JBIG2.…”

Section: B Related Workmentioning

confidence: 99%

Context Coding of Depth Map Images Under the Piecewise-Constant Image Model Representation

Tăbuş

Schiopu

Astola

2013

IEEE Trans. on Image Process.

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Abstract-This paper introduces an efficient method for lossless compression of depth map images, using the representation of a depth image in terms of three entities: 1) the crack-edges; 2) the constant depth regions enclosed by them; and 3) the depth value over each region. The starting representation is identical with that used in a very efficient coder for palette images, the piecewise-constant image model coding, but the techniques used for coding the elements of the representation are more advanced and especially suitable for the type of redundancy present in depth images. Initially, the vertical and horizontal crack-edges separating the constant depth regions are transmitted by 2D context coding using optimally pruned context trees. Both the encoder and decoder can reconstruct the regions of constant depth from the transmitted crack-edge image. The depth value in a given region is encoded using the depth values of the neighboring regions already encoded, exploiting the natural smoothness of the depth variation, and the mutual exclusiveness of the values in neighboring regions. The encoding method is suitable for lossless compression of depth images, obtaining compression of about 10-65 times, and additionally can be used as the entropy coding stage for lossy depth compression.

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“…Liu et al [8] presented two depth compression techniques, the trilateral filter and sparse dyadic mode. Kang et al [9] designed an adaptive geometry-based intra-prediction scheme for depth video coding. Oh et al [10] proposed a depth boundary reconstruction filter to code the depth video.…”

Section: Introductionmentioning

confidence: 99%

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

Peng

Han

Chen

et al. 2016

J Image Video Proc.

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The multi-view video plus depth format is the main representation of a three-dimensional (3D) scene. In the 3D extension of high-efficiency video coding (3D-HEVC), the main framework for depth video is similar to that of color video. However, because of the limitation of the mainstream capture technologies, depth video is inaccurate and inconsistent. In addition, the depth video coding method in the current 3D-HEVC software implementation is highly complex. In this paper, we introduce a joint processing and fast coding algorithm for depth video. The proposed algorithm utilizes the depth and color features to extract depth discontinuous regions, depth edge regions, and motion regions as masks for efficient processing and fast coding. The processing step includes spatial and temporal enhancement. The fast coding method mainly limits the traversal of the CU partition and mode decision. Experimental results demonstrate that the proposed algorithm reduces the coding time and the depth video coding bitrate; the proposed algorithm reduces overall coding time by 44.24 % and depth video coding time by 72.00 % on average. In addition, there is a 24.07 % depth video coding bitrate reduction with an average of 1.65 % Bjontegaard delta bitrate gains.

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Depth Video Coding Using Adaptive Geometry Based Intra Prediction for 3-D Video Systems

Cited by 29 publications

References 11 publications

Efficient Depth Image Compression Using Accurate Depth Discontinuity Detection and Prediction

Efficient Depth Image Compression Using Accurate Depth Discontinuity Detection and Prediction

Context Coding of Depth Map Images Under the Piecewise-Constant Image Model Representation

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

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