K-Means Based Prediction of Transcoded JPEG File Size and Structural Similarity

The problem of efficiently adapting JPEG images to satisfy given constraints such as maximum file size and resolution arises in a number of applications, from universal media access for mobile browsing to multimedia messaging services. However, optimizing for perceived quality (user experience) commands a non-negligible computational cost which in the authors work, they aim to minimize by the use of low-cost predictors. In previous work, the authors presented predictors and predictor-based systems to achieve low-cost and near-optimal adaption of JPEG images under given constraints of file size and resolution. In this work, they extend and improve these solutions by including more information about images to obtain more accurate predictions of file size and quality resulting from transcoding. The authors show that the proposed method, based on the clustering of transcoding operations represented as high-dimensional vectors, significantly outperforms previous methods in accuracy.