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Receiver-and Channel-adaptive Compression for Remote Browsing of Image-Based Scene Representations

ABSTRACT Remote navigation in compressed image-based scene representations requires random access to arbitrary parts of the reference image data to recompose virtual views. The degree of inter-frame dependencies exploited during compression has an impact on the effort needed to access reference images and delimits the rate distortion (RD) trade-off that can be achieved. This work considers conventional RD optimization but additionally takes a given receiver hardware and a maximum available transmission bitrate into account. This leads to an extension of the traditional rate-distortion optimization to a trade-off between the four parameters rate (server side file size), distortion, transmission data rate, and decoding complexity. This RDTC optimization framework allows us to adapt to channel properties and client resources and can significantly improve the user satisfaction in a remote navigation scenario.

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    ABSTRACT: Remote navigation in image-based scene representations requires random access to the compressed reference image data to compose virtual views. When using block-based hybrid video coding concepts, the degree of inter frame dependencies introduced during compression has an impact on the effort that is required to access reference image data and at the same time delimits the rate distortion trade-off that can be achieved. If, additionally, a maximum available channel bitrate is taken into account, the traditional rate-distortion (RD) trade-off can be extended to a trade-off between the storage rate (R), distortion (D), transmission data rate (T), and decoding complexity (C). In this work we present a theoretical analysis of this RDTC space. Experimental results qualitatively match those predicted by theory and show that an adaptation of the encoding process to scenario specific parameters like computational power of the receiver and channel throughput can significantly reduce the user perceived delay or required storage for RDTC optimized streams compared to RD optimized or independently encoded scene representations.
    Packet Video 2007; 12/2007
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    ABSTRACT: Rendering of virtual views in interactive streaming of compressed image-based scene representations requires random access to arbitrary parts of the reference image data. The degree of interframe dependencies exploited during encoding has an impact on the transmission and decoding time and, at the same time, delimits the (storage) rate-distortion (RD) tradeoff that can be achieved. In this work, we extend the classical RD optimization approach using hybrid video coding concepts to a tradeoff between the storage rate (R), distortion (D), transmission data rate (T), and decoding complexity (C). We present a theoretical model for this RDTC space with a focus on the decoding complexity and, in addition, the impact of client side caching on the RDTC measures is considered and evaluated. Experimental results qualitatively match those predicted by our theoretical models and show that an adaptation of the encoding process to scenario specific parameters like computational power of the receiver and channel throughput can significantly reduce the user-perceived delay or required storage for RDTC optimized streams compared to RD optimized or independently encoded scene representations.
    IEEE Transactions on Image Processing 06/2008; 17(5):709-23. · 3.20 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Interactive streaming of compressed image-based scene representations requires random access to the reference image data. The degree of interframe dependencies exploited during encoding has an impact on the transmission and decoding time and, at the same time, delimits the (storage) rate-distortion (RD) tradeoff that can be achieved. The transmission data rate and the decoding complexity at the client have received attention in the literature, but their incorporation into the optimization procedure for compression and streaming is missing. If scenario-specific measures are considered, the traditional RD optimization can be extended to a tradeoff between the (storage) rate (R), distortion (D), transmission data rate (T), and decoding complexity (C). In the first part of this sequel of papers, we have theoretically analyzed the RDTC space for the compression of densely sampled image-based scene representations. In this second part, we consider practical RDTC optimization. We propose a modeling and encoding parameter selection procedure that allows us to adapt the compression to scenario-specific properties. The impact of client side caching is considered and evaluated using an experimental testbed. Our results show a significant reduction of the user perceived delay, memory consumption or required minimum channel and storage bitrate for RDTC optimized streams compared to classical RD optimized or independently encoded scene representations.
    IEEE Transactions on Image Processing 06/2008; 17(5):724-36. · 3.20 Impact Factor

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