Content-aware compression using saliency-driven image retargeting

In this paper we propose a novel method to compress video content based on image retargeting. First, a saliency map is extracted from the video frames either automatically or according to user input. Next, nonlinear image scaling is performed which assigns a higher pixel count to salient image regions and fewer pixels to non-salient regions. The non-linearly downscaled images can then be compressed using existing compression techniques and decoded and upscaled at the receiver. To this end we introduce a non-uniform antialiasing technique that significantly improves the image resampling quality. The overall process is complementary to existing compression methods and can be seamlessly incorporated into existing pipelines. We compare our method to JPEG 2000 and H.264/AVC-10 and show that, at the cost of visual quality in non-salient image regions, our method achieves a significant improvement of the visual quality of salient image regions in terms of Structural Similarity (SSIM) and Peak Signal-to-Noise-Ratio (PSNR) quality measures, in particular for scenarios with high compression ratios.