Re-Ordered FEGC and Block Based FEGC for Inverted File Compression

Data compression has been widely used in many Information Retrieval based applications like web search engines, digital libraries, etc. to enable the retrieval of data to be faster. In these applications, universal codes (Elias codes (EC), Fibonacci code (FC), Rice code (RC), Extended Golomb code (EGC), Fast Extended Golomb code (FEGC) etc.) have been preferably used than statistical codes (Huffman codes, Arithmetic codes etc). Universal codes are easy to be constructed and decoded than statistical codes. In this paper, the authors have proposed two methods to construct universal codes based on the ideas used in Rice code and Fast Extended Golomb Code. One of the authors’ methods, Re-ordered FEGC, can be suitable to represent small, middle and large range integers where Rice code works well for small and middle range integers. It is also competing with FC, EGC and FEGC in representing small, middle and large range integers. But it could be faster in decoding than FC, EGC and FEGC. The authors’ another coder, Block based RFEGC, uses local divisor rather than global divisor to improve the performance (both compression and decompression) of RFEGC. To evaluate the performance of the authors’ coders, the authors have applied their methods to compress the integer values of the inverted files constructed from TREC, Wikipedia and FIRE collections. Experimental results show that their coders achieve better performance (both compression and decompression) for those files which contain significant distribution of middle and large range integers.

Chase-Like Decoding of Arithmetic Codes with Applications

Motivated by recent results in Joint Source/Channel (JSC) coding and decoding, this paper addresses the problem of soft input decoding of Arithmetic Codes (AC). A new length-constrained scheme for JSC decoding of these codes is proposed based on the Maximum a posteriori (MAP) sequence estimation criterion. The new decoder, called Chase-like arithmetic decoder is supposed to know the source symbol sequence and the compressed bit-stream lengths. First, Packet Error Rates (PER) in the case of transmission on an Additive White Gaussian Noise (AWGN) channel are investigated. Compared to classical arithmetic decoding, the Chase-like decoder shows significant improvements. Results are provided for Chase-like decoding for image compression and transmission on an AWGN channel. Both lossy and lossless image compression schemes were studied. As a final application, the serial concatenation of an AC with a convolutional code was considered. Iterative decoding, performed between the two decoders showed substantial performance improvement through iterations.