Efficient Clustering-based Algorithm for Predicting File Size and Structural Similarity of Transcoded JPEG Images

Abstract

The problem of adapting JPEG images to satisfy constraints such as file size and resolution arises in a number of applications, from universal media access to multimedia messaging services. Visually optimized adaptation, however, commands a non-negligible computational cost which we aim to minimize using predictors. In previous works, we presented predictors and systems to achieve low-cost near-optimal adaptation of JPEG images. In this work, we propose a new approach to file size and quality prediction resulting from the Transco ding of a JPEG image subject to changes in quality factor and resolution. We show that the new predictor significantly outperforms the previously proposed solutions in accuracy.