A Codebook Compression Method for Vector Quantization Algorithm

This study introduces a novel approach to enhance the compression ratio of the vector quantization (VQ) algorithm by specifically targeting the compression of its codebook. The VQ algorithm typically generates an index matrix and a codebook to represent compressed images. The proposed method focuses on reducing the size of the codebook, which comprises N codewords, each with elements quantized into four levels. Each 8-bit element in a codeword is compressed to 2-bits, and the encoded codeword is accompanied by the minimum value and a threshold value in the codebook. Experimental results on benchmark color images, such as baboon, airplane, Lena, and others, demonstrate a significant reduction of 62.50% in the size of the VQ codebook

On inverted index compression for search engine efficiency

Efficient access to the inverted index data structure is a key aspect for a search engine to achieve fast response times to users’ queries . While the performance of an information retrieval (IR) system can be enhanced through the compression of its posting lists, there is little recent work in the literature that thoroughly compares and analyses the performance of modern integer compression schemes across different types of posting information (document ids, frequencies, positions). In this paper, we experiment with different modern integer compression algorithms, integrating these into a modern IR system. Through comprehensive experiments conducted on two large, widely used document corpora and large query sets, our results show the benefit of compression for different types of posting information to the space- and time-efficiency of the search engine. Overall, we find that the simple Frame of Reference compression scheme results in the best query response times for all types of posting information. Moreover, we observe that the frequency and position posting information in Web corpora that have large volumes of anchor text are more challenging to compress, yet compression is beneficial in reducing average query response times

The Rabin index of parity games

We study the descriptive complexity of parity games by taking into account the coloring of their game graphs whilst ignoring their ownership structure. Colored game graphs are identified if they determine the same winning regions and strategies, for all ownership structures of nodes. The Rabin index of a parity game is the minimum of the maximal color taken over all equivalent coloring functions. We show that deciding whether the Rabin index is at least k is in PTIME for k=1 but NP-hard for all fixed k > 1. We present an EXPTIME algorithm that computes the Rabin index by simplifying its input coloring function. When replacing simple cycle with cycle detection in that algorithm, its output over-approximates the Rabin index in polynomial time. Experimental results show that this approximation yields good values in practice.Comment: In Proceedings GandALF 2013, arXiv:1307.416