Enhancing the Error Detection Capabilities of DCT Based Codecs using Compressed Domain Dissimilarity Metrics

Farrugia, Reuben A.; Debono, Carl James

doi:10.1109/eurcon.2007.4400260

Cited by 3 publications

(2 citation statements)

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“…The spatial DCT features are based on the fact that there is a correlation between the DCT coefficients of the current block being decoded and the coefficients of the neighboring blocks in space. Therefore, the DCT coefficients of the current MB can be approximated from the surrounding blocks using [30]. Fig.…”

Section: ) Dct Domain Featuresmentioning

confidence: 99%

“…Transmission errors can be detected without any increase in transmission bit rate by exploiting the inherent redundancies within and outside a macroblock [23]- [30] at image level. However, these features achieve low error detection rates when applied individually and some uncorrupted MBs may be detected as erroneous, further reducing the quality of the reconstructed video sequence.…”

Section: Introductionmentioning

confidence: 99%

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A Support Vector Machine Approach for Detection and Localization of Transmission Errors Within Standard H.263++ Decoders

Farrugia

Debono

2009

IEEE Trans. Multimedia

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Wireless multimedia services are increasingly becoming popular boosting the need for better quality-of-experience (QoE) with minimal costs. The standard codecs employed by these systems remove spatio-temporal redundancies to minimize the bandwidth required. However, this increases the exposure of the system to transmission errors, thus presenting a significant degradation in perceptual quality of the reconstructed video sequences. A number of mechanisms were investigated in the past to make these codecs more robust against transmission errors. Nevertheless, these techniques achieved little success, forcing the transmission to be held at lower bit-error rates (BERs) to guarantee acceptable quality. This paper presents a novel solution to this problem based on the error detection capabilities of the transport protocols to identify potentially corrupted group-of-blocks (GOBs). The algorithm uses a support vector machine (SVM) at its core to localize visually impaired macroblocks (MBs) that require concealment within these GOBs. Hence, this method drastically reduces the region to be concealed compared to state-of-the-art error resilient strategies which assume a packet loss scenario. Testing on a standard H.263++ codec confirms that a significant gain in quality is achieved with error detection rates of 97.8% and peak signal-to-noise ratio (PSNR) gains of up to 5.33 dB. Moreover, most of the undetected errors provide minimal visual artifacts and are thus of little influence to the perceived quality of the reconstructed sequences. Index Terms-Error detection and concealment, error resilient coding, learning systems, multimedia communications, video coding, wireless network. Reuben A. Farrugia (S'04) received the first degree in electronic engineering from the University of Malta, Msida, Malta. He is currently pursuing the Ph.D. degree in communications engineering with the Department of Communications and Computer Engineering at the University of Malta. Between 2004 and 2007, he was employed as a Research Engineer with the Department of Communications and Computer Engineering of the University of Malta, and in January 2008, he was appointed Assistant Lecturer with the same department. His research interests are in robust multimedia transmission, multimedia processing, forward error correction codes, and machine learning. Carl James Debono (S'97-M'01-SM'07) received the B.Eng. (Hons.

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Section: ) Dct Domain Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Support Vector Machine Approach for Detection and Localization of Transmission Errors Within Standard H.263++ Decoders

Farrugia

Debono

2009

IEEE Trans. Multimedia

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DCT statistics-based digital dropout detection in degraded archived media

Hoque

Song

Ahn

et al. 2015

Multimed Tools Appl

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A Robust Error Detection Mechanism for H.264/AVC Coded Video Sequences Based on Support Vector Machines

Farrugia

Debono

2008

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-Current trends in wireless communications provide fast and location-independent access to multimedia services. Due to its high compression efficiency, H.264/AVC is expected to become the dominant underlying technology in the delivery of future wireless video applications. The error resilient mechanisms adopted by this standard alleviate the problem of spatio-temporal propagation of visual artifacts caused by transmission errors by dropping and concealing all macroblocks (MBs) contained within corrupted segments, including uncorrupted MBs. Concealing these uncorrupted MBs generally causes a reduction in quality of the reconstructed video sequence. This paper presents a novel error detection algorithm which employs the checksum of the transport layer protocol to detect corrupted segments. Each MB within the corrupted segment is passed through a support vector machine (SVM) classifier to detect and localize visually distorted MBs. The proposed solution was tested on a wide range of video sequences, where on average 95.25% of the residual corrupted MBs which provide annoying visual artifacts were detected. This method reduces the number of uncorrupted MBs to be concealed resulting in a significant gain in quality compared to the standard H.264/AVC decoder.Index Terms-Error-resilient video coding, H.264/ AVC, learning systems, video coding, wireless video transmission.

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Enhancing the Error Detection Capabilities of DCT Based Codecs using Compressed Domain Dissimilarity Metrics

Cited by 3 publications

References 19 publications

A Support Vector Machine Approach for Detection and Localization of Transmission Errors Within Standard H.263++ Decoders

A Support Vector Machine Approach for Detection and Localization of Transmission Errors Within Standard H.263++ Decoders

DCT statistics-based digital dropout detection in degraded archived media

A Robust Error Detection Mechanism for H.264/AVC Coded Video Sequences Based on Support Vector Machines

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