Statistical redundancies have been the dominant target in the image/video compression standards. Perceptually, there exists further redundancies that can be removed to further enhance the compression efficiency. In this paper, we considered short term homogeneous patches that fall into the foveal vision as dynamic textures, for which a psychophysical test was used to estimate their amount of perceptual redundancies. We demonstrated the possible rate saving by utilizing these redundancies. We further designed a learning model that can precisely predict the amount of redundancies and accordingly proposed a generalized perceptual optimization framework.