Coarse-to-fine moving region segmentation in compressed video

Chen, Yue-Meng; Bajić, Ivan V.; Saeedi, Parvaneh

doi:10.1109/wiamis.2009.5031428

Cited by 11 publications

(3 citation statements)

References 9 publications

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“…Depth maps are not as complex as texture images and the use of the Canny edge detector produced very good results in our test cases. We extracted the contours of very precise segmentation maps for the sequences stefan [26, 27] and foreman [28]. This latter case can be seen as the ideal test case.…”

Section: Resultsmentioning

confidence: 99%

Lossless contour coding using elastic curves in multiview video plus depth

Calemme

Cagnazzo

Pesquet‐Popescu

2014

SIP

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Multiview video plus depth is emerging as the most flexible format for three-dimensional video representation, as witnessed by the current standardization efforts by ISO and ITU. In particular, in depth representation, arguably the most important information lies in object contours. As a consequence, an interesting approach consists in performing a lossless coding of object contours, possibly followed by a lossy coding of per-object depth values. In this context, we propose a new technique for lossless coding of object contours, based on the elastic deformation of curves. Using the square-root velocity representation for the elements of the space of curves, we can model a continuous evolution of elastic deformations between two reference contour curves. An elastically deformed version of the reference contours can be sent to the decoder with a reduced coding cost and used as side information to improve the lossless coding of the actual contour. Experimental results on several multiview video sequences show remarkable gains with respect to the reference techniques and to the state of the art.

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Section: Resultsmentioning

confidence: 99%

Lossless contour coding using elastic curves in multiview video plus depth

Calemme

Cagnazzo

Pesquet‐Popescu

2014

SIP

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“…For the segmentation of the foreground objects, the authors in [32] and [33] propose a coarse-to-fine segmentation method for extracting moving regions from compressed video. In the proposed methods, we consider that the foreground objects in the BRF and FRF are already segmented.…”

Section: Proposed Methodsmentioning

confidence: 99%

Fusion of Global and Local Motion Estimation Using Foreground Objects for Distributed Video Coding

Abou-Elailah

Dufaux²,

Farah³

et al. 2015

IEEE Trans. Circuits Syst. Video Technol.

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The side information (SI) in Distributed VideoCoding (DVC) is estimated using the available decoded frames and exploited for the decoding and reconstruction of other frames. The quality of the SI has a strong impact on the performance of DVC. Here, we propose a new approach that combines both global and local SI to improve coding performance. Since the background pixels in a frame are assigned to global estimation and the foreground objects to local estimation, one needs to estimate foreground objects in the SI using the backward and forward foreground objects, the background pixels are directly taken from the global SI. Specifically, elastic curves and local motion compensation are used to generate the foreground objects masks in the SI. Experimental results show that, as far as the rate-distortion performance is concerned, the proposed approach can achieve a PSNR improvement of up to 1.39 dB for a group of picture (GOP) size of 2, and up to 4.73 dB for larger GOP sizes, with respect to the reference DISCOVER codec.

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“…Each data unit represents a full frame in a monoview video or a view in a multiview video. We consider the monoview video datasets Hall Monitor (352 × 288, 30fps) [26,27] and Kimono (1920 × 1080, 24fps), provided by Nakajima Laboratory of the Tokyo Institute of Technology. Both sequences have a GOP size of 1s, namely 30 frames and 24 frames, respectively.…”

Section: A Experimental Setupmentioning

confidence: 99%

Optimal Lagrange multipliers for dependent rate allocation in video coding

Abreu

Cheung

Frossard

et al. 2018

Signal Processing: Image Communication

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In a typical video rate allocation problem, the objective is to optimally distribute a source rate budget among a set of (in)dependently coded data units to minimize the total distortion of all units. Conventional Lagrangian approaches convert the lone rate constraint to a linear rate penalty scaled by a multiplier in the objective, resulting in a simpler unconstrained formulation. However, the search for the "optimal" multiplier-one that results in a distortion-minimizing solution among all Lagrangian solutions that satisfy the original rate constraint-remains an elusive open problem in the general setting.To address this problem, we propose a computation-efficient search strategy to identify this optimal multiplier numerically. Specifically, we first formulate a general rate allocation problem where each data unit can be dependently coded at different quantization parameters (QP) using a previous unit as predictor, or left uncoded at the encoder and subsequently interpolated at the decoder using neighboring coded units. After converting the original rateconstrained problem to the unconstrained Lagrangian counterpart, we design an efficient dynamic programming (DP) algorithm that finds the optimal Lagrangian solution for a fixed multiplier. Finally, within the DP framework, we iteratively compute neighboring singular multiplier values, each resulting in multiple simultaneously optimal Lagrangian solutions, to drive the rates of the computed Lagrangian solutions towards the bit budget. We terminate when a singular multiplier value results in two Lagrangian solutions with rates below and above the bit budget. In extensive monoview and multiview video coding experiments, we show that, for fixed target rate constraints, our DP algorithm and selection of optimal multipliers on average outperform comparable rate control solutions used in video compression standards such as HEVC that do not skip frames in Y-PSNR. Index TermsLagrangian optimization, video and multiview image coding, rate-distortion (RD) optimization.A. De Abreu and P. Frossard are with Signal Processing Laboratory (LTS4), Ecole Polytechnique Fédérale de Lausanne (EPFL),

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Coarse-to-fine moving region segmentation in compressed video

Cited by 11 publications

References 9 publications

Lossless contour coding using elastic curves in multiview video plus depth

Lossless contour coding using elastic curves in multiview video plus depth

Fusion of Global and Local Motion Estimation Using Foreground Objects for Distributed Video Coding

Optimal Lagrange multipliers for dependent rate allocation in video coding

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