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In a recent work, the authors proposed a novel paradigm for interactive video streaming and coined the term JPEG2000-Based Scalable Interactive Video (JSIV) for it. In this work, we investigate JSIV when motion compensation is employed to improve prediction, something that was intentionally left out in our earlier treatment. JSIV relies on three concepts: storing the video sequence as independent JPEG2000 frames to provide quality and spatial resolution scalability, prediction and conditional replenishment of code-blocks to exploit inter-frame redundancy, and loosely coupled server and client policies in which a server optimally selects the number of quality layers for each code-block transmitted and a client makes the most of the received (distorted) frames. In JSIV, the server transmission problem is optimally solved using Lagrangian-style rate-distortion optimization. The flexibility of JSIV enables us to employ a wide variety of frame prediction arrangements, including hierarchical B-frames. JSIV provides considerably better interactivity compared with existing schemes and can adapt immediately to interactive changes in client interests, such as forward or backward playback and zooming into individual frames. Experimental results show that JSIV's performance is inferior to that of SVC in conventional streaming applications while JSIV performs better in interactive browsing applications. [ABSTRACT FROM PUBLISHER]

We present a noniterative multiresolution motion estimation strategy, involving block-based comparisons in each detail band of a Laplacian pyramid. A novel matching score is developed and analyzed. The proposed matching score is based on a class of nonlinear transformations of Laplacian detail bands, yielding 1-bit or 2-bit representations. The matching score is evaluated in a dense full-search motion estimation setting, with synthetic video frames and an optical flow data set. Together with a strategy for combining the matching scores across resolutions, the proposed method is shown to produce smoother and more robust estimates than mean square error (MSE) in each detail band and combined. It tolerates more of nontranslational motion, such as rotation, validating the analysis, while providing much better localization of the motion discontinuities. We also provide an efficient implementation of the motion estimation strategy and show that the computational complexity of the approach is closely related to the traditional MSE block-based full-search motion estimation procedure.