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We present a novel compression scheme for high dynamic range textures, targeted for hardware implementation. Our method encodes images at a constant 8 bits per pixel, for a compression ratio of 6:1. We demonstrate that our method achieves good visual fidelity, surpassing DXTC texture compression of RGBE data which is the most practical method on existing graphics hardware. The decoding logic for our method is simple enough to be implemented as part of the texture fetch unit in graphics hardware.

As mobile smart phones are being equipped with multiple radio interfaces and sensors, they are becoming capable of continuously collecting context information related to the users and the environment. This information can be utilized in different applications and services for providing rich, context-aware experiences. In this paper, we present a client-server platform that enables life logging, via mobile context collection, and processes the data so that meaningful higher-level context can be derived. This can then be mashed-up with 3 rd party Internet services, for providing richer social networking and content associations, keeping privacy and security in mind. We present our core use cases, the proposed end-to-end architecture and current implementation, as well as an explorer user interface that visualizes users' life experience.

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Figure 1: Artifacts resulting from high contrast diagonal edges. Left to right: original, Roimela et al. [2006], our method, andMunkberg et al. [2007]. The method of Roimela et al. exhibits luminance distortion at the edge, whereas the latter two images are lossless for all practical purposes. Image courtesy of OpenEXR (Tree.exr). AbstractWe present a novel compression method for high dynamic range (HDR) textures, targeted for future graphics hardware. Identifying that the existing solutions provide either very high image quality or very simple hardware implementation, we aim to achieve both features in a single solution.Our approach improves upon an existing technique by incorporating a simple chrominance coding that allows overall image quality on par with the state of the art, but at a substantially lower encoding and decoding complexity. The end result is what we believe to be an excellent compromise between image quality and efficiency of hardware implementations.We evaluate our compression method using common test images and established HDR image quality metrics. Additionally, we complement these results with error measurements in the CIE L*a*b* color space in order to separately assess the quality of luminance and chrominance information.