Concealment algorithms for networked video transmission systems

This thesis addresses the problem of cell loss when transmitting video data over an ATM network. Cell loss causes sections of an image to be lost or discarded in the interconnecting nodes between the transmitting and receiving locations. The method used to combat this problem is to use a technique called Error Concealment, where the lost sections of an image are replaced with approximations derived from the information in the surrounding areas to the error. This technique does not require any additional encoding, as used by Error Correction. Conventional techniques conceal from within the pixel domain, but require a large amount of processing (2N2 up to 20N2) where N is the dimension of an N×N square block. Also, previous work at Loughborough used Linear Interpolation in the transform domain, which required much less processing, to conceal the error. [Continues.

Enhancing the error detection capabilities of DCT based codecs using compressed domain dissimilarity metrics

Video compression standards are implemented in wireless data transmission technologies to provide multimedia services efficiently. These compression standards generally utilize the Discrete Cosine Transform (DCT) in conjunction with variable length codes (VLC) in order to achieve the required high compression ratios. While providing the necessary high data rates, this technique has the disadvantage of making the system more susceptible to transmission errors. The standard decoders do not manage to detect a large number of corrupted macroblocks, 40.54% not detected for H.263+, contributing to a significant reduction in the end-to-end video quality as perceived by the end-user. This paper presents three dissimilarity metrics which contain both color and texture information and that can be extracted directly from the compressed DCT coefficients. These metrics can be used to enhance the error-detection capabilities of standard DCT based codecs. Simulation results show that the proposed algorithm increases the error detection rate by 54.06% with a gain in peak signal-to-noise ratio (PSNR) of 3.21 dB. This improvement in performance is superior to other solutions found in literature.peer-reviewe

Enhancing the error detection capabilities of the standard video decoder using pixel domain dissimilarity metrics

The video compression standards commonly adopted in wireless multimedia services utilize variable length codes (VLC) in order to attain high compression ratios. While providing the high data rates required, this technique makes the system more susceptible to transmission errors. Thus the end-to-end quality of the video stream transmitted over an error-prone channel depends on the detection, and concealment of the corrupted macroblocks. The error detection capability of standard decoders is quite limited, for example, in the case of the H.263+ codec around 40.54% of the corrupted macroblocks are undetected, placing a bound on the perceived quality of the reconstructed video sequence. This paper presents a novel solution using eight pixel domain dissimilarity metrics computed in the CIE LUV color space which can be used at decode time to improve the error detection rate of the standard decoder. The spatial dissimilarity metric has been found to perform the best with an average increase in error detection rate of 60.38% when compared to the standard decoder (about 20% more than other published results) with 0% of false detection and a gain in peak signal-to-noise ratio (PSNR) of 3.94 dB.peer-reviewe

Enhancing error resilience in wireless transmitted compressed video sequences through a probabilistic neural network core

Video compression standards commonly employed in the delivery of real-time wireless multimedia services regularly adopt variable length codes (VLCs) for efficient transmission. This coding technique achieves the necessary high compression ratios at the expense of an increased system’s vulnerability to transmission errors. The more frequent presence of transmission errors in wireless channels requires video compression standards to accurately detect, localize and conceal any corrupted macroblocks (MBs) present in the video sequence. Unfortunately, standard decoders offer limited error detection and localization capabilities posing a bound on the perceived video quality of the reconstructed video sequence. This paper presents a novel solution which enhances the error detection and localization capabilities of standard decoders through the application of a Probabilistic Neural Network (PNN). The proposed solution generally outperforms other error detection mechanisms present in literature, as it manages to improve the standard decoder’s error detection rate by up to 95.74%. Index Terms — Error detection coding, learning systems, multimedia communications, video coding, wireless networks.peer-reviewe

A support vector machine approach for detection and localization of transmission errors within standard H.263++ decoders

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.peer-reviewe

An investigation into the requirements for an efficient image transmission system over an ATM network

This thesis looks into the problems arising in an image transmission system when transmitting over an A TM network. Two main areas were investigated: (i) an alternative coding technique to reduce the bit rate required; and (ii) concealment of errors due to cell loss, with emphasis on processing in the transform domain of DCT-based images. [Continues.

Study and simulation of low rate video coding schemes

The semiannual report is included. Topics covered include communication, information science, data compression, remote sensing, color mapped images, robust coding scheme for packet video, recursively indexed differential pulse code modulation, image compression technique for use on token ring networks, and joint source/channel coder design

Robust decoder-based error control strategy for recovery of H.264/AVC video content

Real-time wireless conversational and broadcasting multimedia applications offer particular transmission challenges as reliable content delivery cannot be guaranteed. The undelivered and erroneous content causes significant degradation in quality of experience. The H.264/AVC standard includes several error resilient tools to mitigate this effect on video quality. However, the methods implemented by the standard are based on a packet-loss scenario, where corrupted slices are dropped and the lost information concealed. Partially damaged slices still contain valuable information that can be used to enhance the quality of the recovered video. This study presents a novel error recovery solution that relies on a joint source-channel decoder to recover only feasible slices. A major advantage of this decoder-based strategy is that it grants additional robustness while keeping the same transmission data rate. Simulation results show that the proposed approach manages to completely recover 30.79% of the corrupted slices. This provides frame-by-frame peak signal-to-noise ratio (PSNR) gains of up to 18.1%dB, a result which, to the knowledge of the authors, is superior to all other joint source-channel decoding methods found in literature. Furthermore, this error resilient strategy can be combined with other error resilient tools adopted by the standard to enhance their performance.peer-reviewe

User-Oriented QoS in Packet Video Delivery

We focus on packet video delivery, with an emphasis on the quality of service perceived by the end-user. A video signal passes through several subsystems, such as the source coder, the network and the decoder. Each of these can impair the information, either by data loss or by introducing delay. We describe how each of the subsystems can be tuned to optimize the quality of the delivered signal, for a given available bit rate in the network. The assessment of end-user quality is not trivial. We present recent research results, which rely on a model of the human visual system

Detection of dirt impairments from archived film sequences : survey and evaluations

Film dirt is the most commonly encountered artifact in archive restoration applications. Since dirt usually appears as a temporally impulsive event, motion-compensated interframe processing is widely applied for its detection. However, motion-compensated prediction requires a high degree of complexity and can be unreliable when motion estimation fails. Consequently, many techniques using spatial or spatiotemporal filtering without motion were also been proposed as alternatives. A comprehensive survey and evaluation of existing methods is presented, in which both qualitative and quantitative performances are compared in terms of accuracy, robustness, and complexity. After analyzing these algorithms and identifying their limitations, we conclude with guidance in choosing from these algorithms and promising directions for future research