We propose a novel local correlation based saliency model that is friendly to application of video coding. The
proposed model is developed in YCbCr color space. We extract feature maps with local mean and local contrast
of each channel image and its Gaussian blurred image, and produce rarity maps by calculating the correlation
between the feature maps of the original and blurred channels. The proposed saliency map is produced by a
combination of the local mean rarity maps and the local contrast rarity maps across all the channels. Experiments
validate that the proposed model works with excellent performance.
"A local correlation based visual saliency model", Proc. SPIE 9971, Applications of Digital Image Processing XXXIX, 99712W (28 September 2016); doi: 10.1117/12.2236817; https://doi.org/10.1117/12.2236817