RRED indices: Reduced reference entropic differencing framework for image quality assessment

We study the problem of automatic "reduced reference" image quality assessment algorithms from the point of view of image information change. Algorithms that measure differences between the entropies of wavelet coefficients of reference and distorted images are designed. A family of algorithms are presented, each differing in the amount of data on which information change is predicted and ranging from al most full reference to almost no reference. A special case of this are algorithms that require just a single number from the reference for quality assessment. The algorithms are shown to correlate very well with subjective quality scores as demonstrated on the LIVE Image Quality Assessment Database.