We introduce a novel dictionary optimization method for high-dimensional vector quantization employed in approximate nearest neighbor (ANN) search. Vector quantization methods first seek a series of dictionaries, then approximate each vector by a sum of elements selected from these dictionaries. An optimal series of dictionaries should be mutually independent, and each dictionary should generate a […]

July 8, 2015 by hgpu

Bidirectional Texture Function (BTF) as an effective visual fidelity representation of surface appearance is becoming more and more widely used. In this paper we report on contributions to BTF data compression for multi-level vector quantization. We describe novel decompositions that improve the compression ratio by 15% in comparison with the original method, without loss of […]

June 19, 2015 by hgpu

Multi-trial sampled K-means performance and scalability is studied as a stepping stone towards a Graphical Processing Unit implementation of Entropy Constrained Vector Quantization for interactive data compression. Basic parallelization strategies and data layout impacts are explored with K-means. The K-means implementation is extended to Entropy Constrained Vector Quantization, and additional tuning specific to the anticipated […]

August 1, 2014 by hgpu

Many-Core computing is an actual growing concept that allows the true parallelization of computational tasks. In the particular case of this paper, the vector quantization algorithm was adapted to the many-core concept with the objective of compressing images encoded in the PGM format. For that, a given sequential implementation of the algorithm was optimized and […]

May 21, 2014 by hgpu

In this paper we investigate scalability limitations in the visualization of large-scale particle-based cosmological simulations, and we present methods to reduce these limitations on current PC architectures. To minimize the amount of data to be streamed from disk to the graphics subsystem, we propose a visually continuous level-of-detail (LOD) particle representation based on a hierarchical […]

September 4, 2011 by hgpu

Vector quantization (VQ) is an effective technique applicable in a wide range of areas, such as image compression and pattern recognition. The most time-consuming procedure of VQ is codebook training, and two of the frequently used training algorithms are LBG and self-organizing map (SOM). Nowadays, desktop computers are usually equipped with programmable graphics processing units […]

November 6, 2010 by hgpu