&lt;title&gt;Signal loss recovery in DCT-based image and video codecs&lt;/title&gt;

Wang, Yao; Zhu, Qin-Fan

doi:10.1117/12.50300

Cited by 78 publications

(32 citation statements)

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“…Missing macroblocks can be reconstructed by estimating their low frequency DCT coefficients from the DCT coefficients of the neighboring macroblocks [24,30,31,32], by estimating missing edges in each block from edges in the surrounding blocks as proposed in [34], or by the method of projections onto convex sets [40] as described in [35].…”

Section: Previous Workmentioning

confidence: 99%

“…Post processing (passive) techniques for the sake of error concealment utilize spatial data, or temporal data, or a hybrid of both [5,24,[26][27][28][29][30][31][32][33][34][35][36][37][38][39]. Missing macroblocks can be reconstructed by estimating their low frequency DCT coefficients from the DCT coefficients of the neighboring macroblocks [24,30,31,32], by estimating missing edges in each block from edges in the surrounding blocks as proposed in [34], or by the method of projections onto convex sets [40] as described in [35].…”

Section: Previous Workmentioning

confidence: 99%

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Error Concealment in Encoded Video Streams

Salama

Shroff

Delp

1998

Signal Recovery Techniques for Image and Video Compression and Transmission

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When transmitting compressed video over a data network, one has to deal with how channel errors affect the decoding process. This is particularly problematic with data loss or erasures. In this paper we describe techniques to address this problem in the context of networks where channel errors or congestion can result in the loss of entire macroblocks when MPEG video is transmitted. We describe spatial and temporal techniques for the recovery of lost macroblocks. In particular, we develop estimation techniques for the reconstruction of missing macroblocks using a Markov Random Field model. We show that the widely used heuristic motion compensated error concealment technique based on averaging motion vectors is a special case of our estimation technique. We further describe a technique that can be implemented in real-time.

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Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Error Concealment in Encoded Video Streams

Salama

Shroff

Delp

1998

Signal Recovery Techniques for Image and Video Compression and Transmission

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“…Further examples of applications and environments where image gap restoration can be usefully applied are enhancement of distorted biomedical signals [1], restoration of archived damaged images [2] and packet loss concealment over internet protocol (IP) networks [3].…”

Section: Introductionmentioning

confidence: 99%

Edge-Guided Image Gap Interpolation Using Multi-Scale Transformation

Langari

Vaseghi

Procházka

et al. 2016

IEEE Trans. on Image Process.

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-This paper presents improvements in image gap restoration through incorporation of edge-based directional interpolation within multi-scale pyramid transforms. Two types of image edges are reconstructed; (a) the local edges or textures, inferred from the gradients of the neighbouring pixels and (b) the global edges between image objects or segments, inferred using Canny detector. Through a process of pyramid transformation and downsampling, the image is progressively transformed into a series of reduced size layers until at the pyramid apex the gap size is one sample. At each layer an edge 'skeleton' image is extracted for edge-guided interpolation. The process is then reversed; from the apex, at each layer, the missing samples are estimated (an iterative method is used in the last stage of up-sampling), up-sampled and combined with the available samples of the next layer. Discrete cosine transform and a family of discrete wavelet transforms are utilized as alternatives for pyramid construction. Evaluations over a range of images, in regular and random loss pattern, at loss rates of up to 40%, demonstrate that the proposed method improves PSNR by 1 to 5 dB compared to a range of best published works.Index Terms-Error concealment, multi-scale DCT/DWT pyramid, edge detection, image gap recovery, packet loss concealment. I. INTRODUCTIONmage gap restoration have a wide range of applications that includes in-painting of missing or damaged segments in still images or the replacement of image data packet lost in transmission. Further examples of applications and environments where image gap restoration can be usefully applied are enhancement of distorted biomedical signals [1], restoration of archived damaged images [2] and packet loss concealment over internet protocol (IP) networks [3].A main current application of image gap restoration is packet loss concealment. Packet loss errors may occur due to network congestions or due to signal loss in mobile devices. IP networks are best-effort environments [4,5] where the packet delivery is not guaranteed. The rapid growth in demand for relatively high bandwidth image/video streaming applications over IP networks motivates the need for packet loss recovery and concealment in order to provide more reliable network services and more acceptable user experience [6].There are three broad approaches for mitigating the loss of quality in received images due to packet loss: (a) automatic request for retransmission (ARQ) of the lost packets, (b) error control via forward error correction (FEC) methods and (c) error concealment (EC) methods. The first method retransmits a copy of the damaged/lost packet and results in an increase in bandwidth and delay proportional to error rate [5]. This method can be used on request for retransmission in networks where there is an interaction between sender and receiver. The second category of methods, FEC, employs error correction coding to recover lost pixels from the received information. This implies that the pixel values in successive...

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“…Linear interpolation scheme is often used as an effective method for smoothing image. Furthermore, some adaptive interpolations are presented to achieve maximum smoothness [1], [2]. As a rule, these methods reconstruct lost data from its neighbors in an interpolation surface.…”

Section: Introductionmentioning

confidence: 99%

“…Heuristically, edge pixels selection is based on angle information. Except all the spatial error concealment mentioned above, more spatial error concealments is introduced in [2], [8], [9], [10], [11], [12], [13] etc. For temporal error concealment, residual DCT coefficients and motion vector are often lost due to transmission error.…”

Section: Introductionmentioning

confidence: 99%

Local Binary Pattern Guided Bilateral Spatial Error Reconstruction

Yao¹,

Hong²,

Cheng³

et al. 2013

Proceedings of the 2nd International Symposium on Computer, Communication, Control and Automation

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Abstract-In many communication occasions, packet loss or data corruption often occurs. Data incompleteness results in video degradation, which greatly affect visual quality. Error concealment is needed for the robustness of video transmission. In this paper, we introduce a local binary pattern guided bilateral spatial error concealment. In the computation of local search window, neighboring local binary patterns are computed. The computed binary value is forwarded to bilateral distance weight framework. In bilateral weight computation framework, geometric distance of neighboring patches is replaced by Hamming distance of neighboring local binary patterns. Finally, favorable results of the proposed algorithm are shown in experiments comparing with several competitors.

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<title>Signal loss recovery in DCT-based image and video codecs</title>

Cited by 78 publications

References 0 publications

Error Concealment in Encoded Video Streams

Error Concealment in Encoded Video Streams

Edge-Guided Image Gap Interpolation Using Multi-Scale Transformation

Local Binary Pattern Guided Bilateral Spatial Error Reconstruction

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