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We present a compression scheme for multiview imagery that facilitates high scalability 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 disparity-compensated 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 is subjected to spatial wavelet transforms and embedded block-based 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. [ABSTRACT FROM AUTHOR]

This paper proposes a new method of calculating a matching metric for motion estimation. The proposed method splits the information in the source images into multiple scale and orientation subbands, reduces the subband values to a binary representation via an adaptive thresholding algorithm, and uses mutual information to model the similarity of corresponding square windows in each image. A moving window strategy is applied to recover a dense estimated motion field whose properties are explored. The proposed matching metric is a sum of mutual information scores across space, scale, and orientation. This facilitates the exploitation of information diversity in the source images. Experimental comparisons are performed amongst several related approaches, revealing that the proposed matching metric is better able to exploit information diversity, generating more accurate motion fields. [ABSTRACT FROM AUTHOR]

In this paper, we propose the use of “motion hints” to produce interframe predictions. A motion hint is a loose and global description of motion that can be communicated using metadata; it describes a continuous and invertible motion model over multiple frames, spatially overlapping other motion hints. A motion hint provides a reasonably accurate description of motion but only a loose description of where it is applicable; it is the task of the client to identify the exact locations where this motion model is applicable. The focus of this paper is a probabilistic multiscale approach to identifying these locations of applicability; the method is robust to noise, quantization, and contrast changes. The proposed approach employs the Laplacian pyramid; it generates motion hint probabilities from observations at each scale of the pyramid. These probabilities are then combined across the scales of the pyramid starting from the coarsest scale. The computational cost of the approach is reasonable, and only the neighborhood of a pixel is employed to determine a motion hint probability, which makes parallel implementation feasible. This paper also elaborates on how motion hint probabilities are exploited in generating interframe predictions. The scheme of this paper is applicable to closed-loop prediction, but it is more useful in open-loop prediction scenarios, such as using prediction in conjunction with remote browsing of surveillance footage, communicated by a JPEG2000 Interactive Protocol (JPIP) server. We show that the interframe predictions obtained using the proposed approach are good both visually and in terms of PSNR. [ABSTRACT FROM AUTHOR]

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]