Tomi Aarnio

High dynamic range texture compression

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.

Efficient high dynamic range texture compression

We 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.

Enhancing end-user experience in a multi-device ecosystem

A person may use many devices capable of rendering digital content on a regular basis. For instance, the user is in front of a large TV screen, and soon he or she moves away from the TV and wants to continue the media consumption. The transfer of media content across devices needs to be handled seamlessly. In this paper, we present a phone-centric approach to improve the end-user experience in multi-device ecosystem. Together with predefined parameters, we use context information to trigger content adaptation, and generate decisions relating to the transcoding operation. We have made a preliminary UI evaluation of the system, and the system was found to be useful, although requiring fine tuning and further development.

Towards real-time applications in mobile web browsers

WebGL and WebCL are web targeted versions of OpenGL ES and OpenCL standards. Using these standards, it is possible to better exploit the hardware resources in embedded systems from web browsers allowing timely processing of audio, video, and graphics. WebGL excels in graphics applications while WebCL fares better when more flexibility is required in execution platform selection, load balancing, data formats, control flow, or memory access patterns. This paper explores the potential for mobile web application acceleration utilizing WebGL and particularly WebCL which is currently under intense development. Where driver support is lacking, WebGL is used as a proxy to provide an estimate of WebCL opportunity. Speedups in the order of 200x over JavaScript are demonstrated in best case situations for a GPU target. In similar situations, CPU acceleration can be 10x while running in a laptop browser. In addition, as building and optimizing a WebCL implementation is part of the reported work, an overview of the important development issues is given.

Efficient floating-point texture decompression

We propose a novel hardware design for decoding compressed floating-point textures in a graphics processing unit (GPU). Our decoder is based on the NXR texture format, which provides lossy, fixed-rate 6∶1 compression for floating-point textures. Our design exploits the constraints of the compressed pixel blocks to produce the correct output using only fixed-point arithmetic. This results in significantly lower silicon area occupation compared to pre-existing floating-point texture decoders.