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

Abstract

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.