Image Compression by Learning to Minimize the Total Error

In this paper, we consider the problem of lossy image compression. Recently, machine learning techniques have been introduced as effective mechanisms for image compression. The compression involves storing only the grayscale image and a few carefully selected color pixel seeds. For decompression, regression models are learned with the stored data to predict the missing colors. This reduces image compression to standard active learning and semisupervised learning problems. In this paper, we propose a novel algorithm that makes use of all the colors (instead of only the colors of the selected seeds) available during the encoding stage. By minimizing the total color prediction error, our method can achieve a better compression ratio and better colorization quality than previous methods. The experimental results demonstrate the effectiveness of our proposed algorithm.