We present a compression scheme for multiview imagery that facilitates high scalablity and accessibility of the compressed content. Our scheme relies upon constructing at a single base view a disparity model for a group of views and then utilizing this base-anchored model to infer disparity at all views belonging to the group. We employ a hierarchical disparitycompensated inter-view transform where the corresponding analysis and synthesis filters are applied along the geometric flows defined by the base-anchored disparity model. The output of this inter-view transform along with the disparity information are subjected to spatial wavelet transforms and embedded blockbased coding. Rate-distortion results reveal superior performance to the x.265 anchor chosen by the JPEG Pleno standards activity for the coding of multiview imagery captured by high density camera arrays.

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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.

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A video stored as a sequence of JPEG2000 images can provide the scalability, flexibility, and accessibility that is lacking in current predictive motion-compensated video coding standards; however, streaming this sequence would consume considerably more bandwidth. This paper presents a new method for optimized streaming of a JPEG2000 video that relies on motion compensation and server-optimized conditional replenishment to reduce temporal redundancy, in collaboration with an intelligent client policy for reconstructing the available content. In particular, we propose transmission of motion vectors and an optimized number of layers, possibly zero, for each code-block of the JPEG2000 representation of each new frame. We also propose the use of a sliding window to optimize a group of frames such that codeblocks of these frames have more than one chance of being enhanced if that is beneficial to subsequent frames. Ratedistortion optimization in the Lagrangian sense is employed to achieve the lowest possible MSE. It is expected that mobile clients with their limited processing powers would benefit from this work in real-time and interactive applications, such as teleconferencing and surveillance. This paper introduces the concept, formulates optimization criteria, and compares the performance with alternative strategies.