Compound Gain

Compound Gain (CG) is a visual distinctness metric for evaluating the performance of compression methods. It has been developed by Computer Vision Group at the University of Granada in Spain.
 
Various distinctness metrics have been proposed to compare and rank target detectability, and to quantify background or scene complexity. As a matter of fact it is of great practical value to have computational visual differences or distinctness measures which can be applied to evaluate image displays, (virtual) scene generators, image compression methods, image reproduction methods, camouflage measures, and traffic safety devices. Relevant computational models of early human vision typically process an input image through various bandpass filters and analyze first order statistical properties of the filtered images to compute a target distinctness metric. If they give good predictors of target saliency for humans performing visual search and detection tasks, they may be used to compute visual distinctness of image subregions (target areas) from digital imagery.