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

Abstract

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.