Efficient Depth Image Compression Using Accurate Depth Discontinuity Detection and Prediction

Farrugia, Reuben A.

doi:10.1109/sitis.2012.15

Cited by 3 publications

(11 citation statements)

References 19 publications

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“…The depth map compression algorithm presented in this paper extends the method presented by the same author in [12]. Fig.…”

Section: System Overviewmentioning

confidence: 80%

“…This method manages to significantly reduce the amount of information that needs to be transmitted achieving a Peak Signal-to-Noise Ratio (PSNR) gain of up to 6.6 dB relative to JPEG and reduce the lower bound on the data rate by around 0.38 bpp. Moreover, the proposed system manages to outperform the Zanuttigh-Cortelazzo method [11] and has a slight edge over the method presented in [12].…”

mentioning

confidence: 89%

“…The authors in [11] has presented a depth segmentation based algorithm which was later on improved by Farrugia in [12]. These depth map compression algorithms manage to preserve the edge discontinuities to achieve high quality virtual views.…”

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confidence: 99%

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Exploiting color-depth image correlation to improve depth map compression

Farrugia

Gambin

2013

Eurocon 2013

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Abstract-The multimedia signal processing community has recently identified the need to design depth map compression algorithms which preserve depth discontinuities in order to improve the rendering quality of virtual views for Free Viewpoint Video (FVV) services. This paper adopts contour detection with surround suppression on the color video to approximate the foreground edges present in the depth image. Displacement estimation and compensation is then used to improve this prediction and reduce the amount of side information required by the decoder. Simulation results indicate that the proposed method manages to accurately predict around 64% of the blocks. Moreover, the proposed scheme achieves a Peak Signal-to-Noise Ratio (PSNR) gain of around 4.9 -6.6 dB relative to the JPEG standard and manages to outperform other state of the art depth map compression algorithms found in literature.Index Terms-3D television, depth map compression, displacement estimation and compensation, edge detection, surround suppression I. INTRODUCTION Advances in technology have contributed to the development of new applications such as 3D Television (3DTV) and Free Viewpoint Video (FVV), which have recently started to attract interest within the marketplace. The multi-view video plus depth (MVD) [1] format enables the generation of an infinite set of videos using a finite set of color and corresponding depth videos [2]. The MVD format significantly reduces the amount of information needed to deploy these services. Moreover, in order to make these applications more viable, both color and depth videos need to be compressed. However, the quality of the rendered views is significantly dependent on the quality of the depth video.Traditional image and video compression standards produce blurring artifacts along depth discontinuities, which negatively affect the Quality of Experience (QoE) of the rendered virtual views [3], [4]. The authors in [5] have demonstrated that higher rendering quality can be perceived when employing depth map compression strategies which preserve edge boundaries. Following this observation the same authors have proposed to reshape the dynamic range and use Region of Interest (RoI) coding to improve the performance of traditional image compression standards. Directional transforms were considered in [3], [6] which are highly computational intensive.

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“…The depth map compression algorithm presented in this paper extends the method presented by the same author in [12]. Fig.…”

Section: System Overviewmentioning

confidence: 80%

mentioning

confidence: 89%

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Exploiting color-depth image correlation to improve depth map compression

Farrugia

Gambin

2013

Eurocon 2013

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“…The Depth Discontinuity Predictor, which is similar to the one adopted in [10], groups the neighbouring segment blocks S m−1,n , S m−1,n−1 , S m,n−1 , S m+1,n−1 in a set S ζ while the corresponding depth blocks D m−1,n ,D m−1,n−1 ,D m,n−1 ,D m+1,n−1 are grouped within a setD ζ . The local cluster mean of every segment k ∈ K is computed usingμ…”

Section: Intra Depth Predictionmentioning

confidence: 99%

“…Directional transforms were adopted in [7], [8] which are highly computational intensive. Depth map prediction schemes were proposed in [9], [10] which were found to provide high quality depth maps which significantly improved the rendered video quality. However, these methods are not compatible with existing video coding standards.…”

Section: Introductionmentioning

confidence: 99%

Depth coding using depth discontinuity prediction and in-loop boundary reconstruction filtering

Farrugia

Hili

2014

2014 IEEE Visual Communications and Image Processing Conference

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Abstract-This paper presents a depth coding strategy that employs K-means clustering to segment the sequence of depth images into K clusters. The resulting clusters are losslessly compressed and transmitted as supplemental enhancement information to aid the decoder in predicting macroblocks containing depth discontinuities. This method further employs an in-loop boundary reconstruction filter to reduce distortions at the edges. The proposed algorithm was integrated within both H.264/AVC and H.264/MVC video coding standards. Simulation results demonstrate that the proposed scheme outperforms the state of the art depth coding schemes, where rendered Peak Signal to Noise Ratio (PSNR) gains between 0.1 dB and 0.5 dB were observed.

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Compression of Noisy Depth Image using Planes

Modi

Kalra

Kumar

2014

Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing

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This paper investigates compression of depth images -particularly, noisy depth images -captured by depth sensors like Kinect. Our scheme is based on incrementally detecting planes in depth images and then storing the plane parameters instead of the depth information of the pixels lying on the detected planes. Residuals are then computed and compressed using standard image compression techniques. Our technique incorporates the input error model for comprehensive and accurate plane detection. Thereby, this accounts for the reliability of the input data in the compression scheme. The plane detection also accounts for edges. Experiments exhibit better image quality than standard compression techniques with smaller error. We additionally propose a novel error metric to evaluate compression of noisy depth images.

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Efficient Depth Image Compression Using Accurate Depth Discontinuity Detection and Prediction

Cited by 3 publications

References 19 publications

Exploiting color-depth image correlation to improve depth map compression

Exploiting color-depth image correlation to improve depth map compression

Depth coding using depth discontinuity prediction and in-loop boundary reconstruction filtering

Compression of Noisy Depth Image using Planes

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