Perceptually optimized error resilient transcoding using attention-based intra refresh

While deployment of wireless channels has become widespread and fast-growing for mobile applications, transmitting data over these existing error-prone networks can be very unreliable and challenging due to time-varying interference and channel errors. Many error-resilient algorithms have been proposed to provide adequate resilient features in order to protect video data from channel errors. However, these algorithms often aim to achieve the optimal decoded video quality in terms of mean square error without any consideration for the visual quality. In this paper, we present a perceptually error-resilient method for video transcoding based on the attention-based intra refresh technique and the characteristics of the human visual system to enhance the perceptual performance of the transcoded video. Specifically, the foveated just noticeable distortion and visual attention models are employed to estimate the perceptual loss impact due to error propagation for allocating intra-refreshed macroblocks in the transcoded video. Experimental results show that the proposed method can achieve a much better performance than the existing methods in terms of both the visual quality and perceptual quality measure.