Support Vector Machine based fusion for multi-view Distributed Video Coding

Dufaux, Frédéric

doi:10.1109/icdsp.2011.6005004

Cited by 11 publications

(17 citation statements)

References 24 publications

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“…SI generated with DISCOVER along the view axis; Comparing all these methods allows us to have a deeper insight on the contribution of all the proposed tools to the final performance. The FD method is considered the reference since its performance is better than much of the state of the art and only slightly worse than (but very close to) more recent fusion techniques, as shown in [49].…”

Section: Results For Fusion With Occlusion Detectionmentioning

confidence: 99%

“…This happens for example for sequences where one interpolation is much better than the other (as shown for the 'outdoor' sequence in [49] and in our experimental section). For these sequences, the correlation along one axis (temporal or inter-view) is much stronger than the correlation along the other.…”

Section: Adaptive Validationmentioning

confidence: 98%

“…Recently, support vector machine was proposed by Dufaux [49] for fusion, which is considered as a classification problem with two classes. The features are extracted from the four KFs surrounding the WZF that has to be estimated.…”

Section: Fusion Techniquesmentioning

confidence: 99%

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Novel solutions for side information generation and fusion in multiview DVC

Petrazzuoli

Cagnazzo

Pesquet‐Popescu

2013

EURASIP J. Adv. Signal Process.

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One of the key problems in distributed video coding is the generation of side information. This task consists of producing an estimate of an image with some neighboring ones, such as those taken by the same camera at different time instants, or, in the case of multiview setups, images taken at the same time instant by different cameras. If both estimates are available, a further problem arises, which is how to merge them in order to create a single side information. This problem is very relevant since a good estimate of the unknown image will require only a few bits to be corrected. Considering a multiview distributed video-coding setup, we propose a novel technique for inter-view interpolation based on occlusion prediction, a new fusion technique from multiple estimates, and finally an adaptive validation step for switching among the three possible side information images: temporal, inter-view, and fusion. We provide a comprehensive set of experimental results, which indicate bit rate reductions of more than 9% in average; moreover, we observe much more consistent results with respect to state-of-the-art techniques.

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Section: Results For Fusion With Occlusion Detectionmentioning

confidence: 99%

Section: Adaptive Validationmentioning

confidence: 98%

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Novel solutions for side information generation and fusion in multiview DVC

Petrazzuoli

Cagnazzo

Pesquet‐Popescu

2013

EURASIP J. Adv. Signal Process.

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“…On the other hand, in DVC solutions for the multiview scenario, each camera can independently code the frames, relying on the decoder to exploit the correlation between the views [8,9]. Typically, the multiview DVC (M-DVC) decoder tries to exploit, at the same time, temporal intraview and inter-view correlation, generating two SI frames: (1) temporal SI, by means of motion estimation and interpolation, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…employing overlapped block motion compensation (OBMC) [6] and (2) inter-view SI, generated by leveraging the inter-view redundancy [3]. To exploit the best part of each estimated SI frame, it is necessary to fuse the frames, choosing the best regions of each estimated SI frame to create a final SI frame that is used for decoding [8,9]; typically, the regions are chosen according to an estimation of their quality. SI fusion is a hard problem, and there are many fusion techniques available in the literature [8] with various degrees of efficiency.…”

Section: Introductionmentioning

confidence: 99%

A robust fusion method for multiview distributed video coding

Salmistraro

Ascenso²,

Brites³

et al. 2014

EURASIP J. Adv. Signal Process.

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Distributed video coding (DVC) is a coding paradigm which exploits the redundancy of the source (video) at the decoder side, as opposed to predictive coding, where the encoder leverages the redundancy. To exploit the correlation between views, multiview predictive video codecs require the encoder to have the various views available simultaneously. However, in multiview DVC (M-DVC), the decoder can still exploit the redundancy between views, avoiding the need for inter-camera communication. The key element of every DVC decoder is the side information (SI), which can be generated by leveraging intra-view or inter-view redundancy for multiview video data. In this paper, a novel learning-based fusion technique is proposed, which is able to robustly fuse an inter-view SI and an intra-view (temporal) SI. An inter-view SI generation method capable of identifying occluded areas is proposed and is coupled with a robust fusion system able to improve the quality of the fused SI along the decoding process through a learning process using already decoded data. We shall here take the approach to fuse the estimated distributions of the SIs as opposed to a conventional fusion algorithm based on the fusion of pixel values. The proposed solution is able to achieve gains up to 0.9 dB in Bjøntegaard difference when compared with the best-performing (in a RD sense) single SI DVC decoder, chosen as the best of an inter-view and a temporal SI-based decoder one.

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Image Fusion Techniques: A Survey

Kaur

Koundal

Kadyan

2021

Arch Computat Methods Eng

227

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The necessity of image fusion is growing in recently in image processing applications due to the tremendous amount of acquisition systems. Fusion of images is defined as an alignment of noteworthy Information from diverse sensors using various mathematical models to generate a single compound image. The fusion of images is used for integrating the complementary multi-temporal, multi-view and multi-sensor Information into a single image with improved image quality and by keeping the integrity of important features. It is considered as a vital pre-processing phase for several applications such as robot vision, aerial, satellite imaging, medical imaging, and a robot or vehicle guidance. In this paper, various state-of-art image fusion methods of diverse levels with their pros and cons, various spatial and transform based method with quality metrics and their applications in different domains have been discussed. Finally, this review has concluded various future directions for different applications of image fusion.

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Support Vector Machine based fusion for multi-view Distributed Video Coding

Cited by 11 publications

References 24 publications

Novel solutions for side information generation and fusion in multiview DVC

Novel solutions for side information generation and fusion in multiview DVC

A robust fusion method for multiview distributed video coding

Image Fusion Techniques: A Survey

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