Neural network based image coding quality prediction
Pascal Fleury, Oliver Egger · 2002
Developments in digital image coding tend to involve more and more complex algorithms, and require therefore an increasing amount of computation. To improve the overall system performance, some schemes apply different coding algorithms to separate parts of an image according to the content of this subimage. Such schemes are referred to as dynamic coding schemes. Applying the best suited coding algorithm to a part of an image will lead to an improved coding quality, but implies an algorithm selection phase. Current selection methods require the computation of the reconstructed image after coding and decoding with all the selected algorithms in order to choose the best method. Some other schemes use ways of pruning the search in the algorithm space. Both approaches suffer from a heavy computational load. Furthermore, the computational complexity is increased even more if the parameters have to be adjusted for a given algorithm during the search. This paper describes a way to predict the coding quality of a region of the input image for any given coding method. The system will then be able to select the best suited coding algorithm for each region according to the predicted quality. This prediction scheme has low complexity, and also enables the adjustment of algorithm specific parameters during the search.