To enable the delivery of multimedia content to mobile devices with limited capabilities, high volume transcoding servers must rely on efficient adaptation algorithms. Our objective in addressing the case of JPEG image adaptation was to find computationally efficient algorithms to accurately predict the compressed file size of images subject to simultaneous changes in quality factor (QF) and resolution. In this paper, we present two new prediction algorithms which use only information readily available from the file header. The first algorithm, QF Scaling-Aware Prediction, predicts file size based on the QF of the original picture, as well as a target QF and scaling. The second algorithm, Clustered QF Scaling-Aware Prediction, also takes into account the resolution of the original picture for improved prediction accuracy. As both algorithms rely on machine-learning strategies, a large corpus of representative JPEG images was assembled. We show that both prediction algorithms lead to acceptably small relative prediction errors in adaptation scenarios of interest.

Abstract-Today, professional documents, created in applications such as PowerPoint and Word, can be shared using ubiquitous mobile terminals connected to the Internet. GoogleDocs and EasyMeet are good examples of such collaborative Web applications dedicated to professional documents. The static adaptation of professional documents has been studied extensively. Dynamic adaptation can be very useful and practical for interactive multimedia applications, because it allows the delivery of highly customized content to the end-user without the need to generate and store multiple transcoded versions. In this paper, we propose a dynamic framework that enables us to estimate transcoding parameters on the fly in order to generate near-optimal adapted content for each user. The framework is compared to current dynamic methods as well as to static adaptation solutions. We show that the proposed framework provides a better trade-off between quality and storage compared to other static and dynamic approaches. To quantify the quality of the adapted content, we introduce a measure of the quality of the experience based on its visual quality of the adapted content, as well as on the impact of its total delivery time. The framework has been tested on (but is not limited to) OpenOffice Impress presentations.

Abstract-Reducing the file size of a JPEG image to meet bandwidth or terminal constraints is a common transcoding operation. The reduction can be achieved by reducing either the quality factor (QF) or the resolution, or both. In this paper, we analyze the impact of QF and scaling parameter choices on the quality of the resulting images, as measured by a quality metric such as the Structural SIMilarity index (SSIM). We propose a quality-aware transcoding system which considers the quality of transcoded images when QF and scaling are selected jointly. Its goal is to select QF and scaling parameters that maximize the user experience under a given viewing condition, as measured by the chosen quality metric.