Multiple Distortion Measures for Packetized Scalable Media

Abstract

Abstract-As the diversity in end-user devices and networks grows, it becomes important to be able to efficiently and adaptively serve media content to different types of users. A key question surrounding adaptive media is how to do RateDistortion optimized scheduling. Typically, distortion is measured with a single distortion measure, such as the Mean-Squared Error compared to the original high resolution image or video sequence. Due to the growing diversity of users with varying capabilities such as different display sizes and resolutions, we introduce Multiple Distortion Measures to account for a diverse range of users and target devices. Multiple Distortion Measures (MDM) gives a clear framework with which to evaluate the performance of media systems which serve a variety of users. Scalable coders, such as JPEG2000 and H.264/MPEG-4 SVC, allow for adaptation to be performed with relatively low computational cost. We show that accounting for MDM can significantly improve system performance; furthermore, by combining this with scalable coding, this can be done efficiently. Given these Multiple Distortion Measures, we propose an algorithm to generate embedded schedules, which enables low-complexity, adaptive streaming of scalable media packets to minimize distortion across multiple users. We show that using MDM achieves up to 4dB gains for spatial scalability applied to images and 12dB gains for temporal scalability applied to video.Index Terms-Multiple distortion measures, scalable streaming, embedded packet schedules, rate-distortion optimization, JPEG2000, H.264/MPEG-4 SVC

Scheduling algorithms for broadcasting media with multiple distortion measures

“…However, this ignores the case of different user types who measure distortion in various ways-hence, the need for Multiple Distortion Measures [1]. The latter arise due to the increase in diversity of multimedia consumers.…”

“…Suppose user u does not receive packets in the set K. Then his total incurred distortion is: Fig. 2 for a more detailed description see [1]. We define by T u (X) a transformation operator of media content, X, for user type u.…”

“…How to evaluate performance of a scalable media system is examined in [1], [6]. To account for the varying display capabilities of each user, we consider media packets with Multiple Distortion Measures [1].…”

“…By considering the viewing capabilities of each user when making a scheduling decision, up to 4dB gains can be achieved in Multiple Distortion Measure aware embedded schedules [1]. Embedded schedules are schedules which incrementally add packets so that all packets in the schedule at rate R 1 are also included in the schedule at rate R 2 > R 1 .…”

Wireless Video Broadcasting to Diverse Users

“…To account for the varying devices of each user, we consider media packets with Multiple Distortion Measures [1], [2]. Multiple Distortion Measures capture the varying capabilities of each user.…”