A Fast and Accurate Prediction of BPG Compression Parameters in Optimal Operation Point Neighbourhood for Three-channel Noisy Images

This chapter considers the lossy compression of three-channel images corrupted by additive white Gaussian noise. The better portable graphics (BPG) encoder that has three different operation modes is studied. As for other encoders, a noise filtering effect is observed and it influences the encoder performance. For certain conditions (image complexity and noise intensity), the so-called optimal operation point (OOP) may exist. The main property of OOP is that the quality of a compressed image (calculated with respect to the true image) is better than the quality of the corresponding noisy image according to a given metric (either conventional such as mean square error or some visual quality metric). Knowledge concerning the OOP existence and compression parameters in it for a given noisy image can be useful. In the case of OOP existence, it is reasonable to compress a given image in OOP or its neighborhood. If OOP does not exist, a more “careful” lossy compression is expedient. The problem is that in practice the true image is not available, and, thus, it is impossible to calculate full-reference metrics for compressed and noise-free images. Meanwhile, we show in this paper that it is possible to predict compression parameters for a given noisy image in advance. Moreover, this prediction is fast and accurate enough for a decision undertaking on what coder parameter to set to reach OOP. In addition, in this paper, we demonstrate that it is possible to predict the compression parameters not only in OOP but also in its neighborhood. This allows for undertaking more reliable decisions. Prediction opportunities are shown for two visual quality metrics and three typical modes of the BPG coder operation: 4:4:4, 4:2:2, and 4:2:0. One novel way of prediction accuracy improvement based on the joint use of two input parameters is shown.