In past years, several visual saliency algorithms have been proposed to extract salient regions from multimedia content in view of practical applications. Entropy is one of the important measures to extract salient regions, as these regions have high randomness and attract more visual attention. In the context of perceptual video coding (PVC), computational visual saliency models that utilize the charactertistics of the human visual system to improve the compression ratio are of paramount importance. To date, only a few PVC schemes have been reported that use the visual saliency model. In this paper, we conduct the first attempt to utilize entropy based visual saliency models within the high efficiency video coding (HEVC) framework. The visual saliency map generated for each input video frame is optimally thresholded to generate a binary saliency mask. The proposed HEVC compliant PVC scheme adjusts the quantization parameter according to visual saliency relevance at the coding tree unit (CTU) level. Efficient CTU level rate control is achieved by allocating bits to salient and non-salient CTUs by adjusting the quantization parameter values according to their perceptual weighted map. The attention based on information maximization has shown the best performance on newly created ground truth dataset, which is then incorporated in a HEVC framework. An average bitrate reduction of
is achieved by the proposed HEVC compliant PVC scheme with the same perceptual quality and a nominal increase in coding complexity of
when compared with HEVC reference software. Moreover, the proposed PVC scheme performs better than other HEVC based PVC schemes when encoded at low data rates.
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