MSE Prediction in BPG-Based Lossy Compression of Grayscale Images

Visual information, such as images, is one of the most important parts of the digital realm. Its volume quickly increases due to better resolution of imagers and a large amount of images acquired each day. The acquired images should be stored on personal computers, servers, etc., and/or transferred via communication lines. A common way to reduce the data size is to apply compression techniques where lossy compression is often preferable because it is able to provide a significantly higher compression ratio compared to lossless compression. Meanwhile, it is necessary to control quality of lossy compressed images to avoid (minimize) negative consequences of introduced distortions. To solve this task, this paper proposes ways to predict mean squared error (MSE) of introduced distortions for better portable graphics (BPG) lossy compression. Such a prediction can be helpful in scenarios when an image has to be compressed in visually lossless manner or an appropriate compromise between the attained compression ratio (CR) and compressed image quality should be produced. It is shown that MSE is highly correlated not only with compression control parameter Q used in the BPG encoder, but with characteristics (complexity) of an image to be compressed. We present and compare several approaches to MSE prediction based on image fast preliminary analysis using local image activity and entropy. The practical recommendations for the MSE prediction are given.