Image quality prediction for bitrate allocation

In image coding, the choice of a good image coding algorithm is very dependent on the image content. Based on this fact, dynamic coding algorithms have been designed. They try to find an optimal coding scheme for each image segment. They rely on an exhaustive search of the best coding algorithm. Evaluation of all algorithms is computationally very intensive and strongly limits the number of considered algorithms for a given application. Therefore, current standards rely on a single coding algorithm. This paper investigates a way to predict the coding quality from the image content. This prediction is based on a neural network. The coding quality is computed from image region features. Those features are easy and fast to compute, and are common to the whole set of considered coding algorithms. Therefore, the choice of the best algorithm can be based on those predicted coding qualities, and does not require the computation of all coding algorithms. The system is also fast enough to be used for dynamic bitrate allocation, and a simple algorithm to do this is proposed