Jukka Teuhola

A compression method for clustered bit-vectors

A bit-vector can be compressed, if the frequence of zeroes (or ones as well) differs from 0.5 or if the vector is clustered in some way (i.e. not random). There are several compression methods, some of which are represented in the references [l-3]. The methods can be divided into three types: (1) Fixed-to-variable ncoding: The bit-vector is divided into futed-length sub-vectors, which are replaced with variable-length codewords. (2) Variizble-to-fuced encoding: The bit-vector is divided into sub-vectors, so-called runs, which consist of consecutive O-bits terminating with a l-bit (or vice versa). The number of the O-bits is called run-length and it is represented with a fured-length number. (3) Variizble-to-variable encoding: The run-length is encoded to a variable-length codeword. The efficiency of a compression method can be expressed with compression gain, which simply means the idO.

The external Heapsort

Heapsort is an internal sorting method which sorts an array of n records in place in O(n log n) time. Heapsort is generally considered unsuitable for external random-access sorting. By replacing key comparisons with merge operations on pages, it is shown how to obtain an in-place external sort which requires O(m log m) page references, where m is the number of pages which the file occupies. The new sort method (called Hillsort) has several useful properties for advanced database management systems. Not only does Hillsort operate in place, i.e., no additional external storage space is required assuming that the page table can be kept in core memory, but accesses to adjacent pages in the heap require one seek only if the pages are physically contiguous. The authors define the Hillsort model of computation for external random-access sorting, develop the complete algorithm and then prove it correct. The model is next refined and a buffer management concept is introduced so as to reduce the number of merge operations and page references, and make the method competitive to a basic balanced two-way external merge. Performance characteristics are noted such as the worst-case upper bound, which can be carried over from Heapsort, and the average-case behavior, deduced from experimental findings. It is shown that the refined version of the algorithm which is on a par with the external merge sort. >

Predictive test compression by hashing

The knowledge of a short substring constitutes a good basis for guessing the next character in a natural language text. This observation, i.e. repeated guessing and encoding of subsequent characters, is very fundamental for the predictive text compression. The paper describes a family of such compression methods, using a hash table for searching the prediction information. The experiments show that the methods produce good compression gains and, moreover, are very fast. The one-pass versions are especially apt for “on-the-fly” compression of transmitted data, and could be a basis for specialized hardware.

Syntax-directed compression of program files

Parsing can be applied to compress source programs. A suitably encoded parse tree, together with the symbol table, constitutes a very compact representation of the program. The paper reports a Prolog implementation of the method, including automatic, syntax‐directed, encoder and decoder generators. The test results show compression gains of 50–60 per cent.

A linear expected-time algorithm for computing planar relative neighbourhood graphs

Abstract A new algorithm for computing the relative neighbourhood graph (RNG) of a planar point set is given. The expected running time of the algorithm is linear for a point set in a unit square when the points have been generated by a homogeneous planar Poisson point process. The worst-case running time is quadratic on the number of the points. The algorithm proceeds in two steps. First, a supergraph of the RNG is constructed with the aid of a cell organization of the points. Here, a point is connected by an edge to some of its nearest neighbours in eight regions around the point. The nearest region neighbours are chosen in a special way to minimize the costs. Second, extra edges are pruned from the graph by a simple scan.

Complexity Analysis of H.264 Decoder for FPGA Design

A major challenge in the design of any real time system is the proper selection of implementation and platform alternatives. In this paper, a suitable FPGA-based design of the H.264 decoder is presented. Since H.264 standard only specifies the syntax and semantics of the video stream and not the video codec itself, the selection process may be directed based upon the temporal complexity of different parts of the decoder. Here, we present the process flow of these parts using basic algebraic operators. The analysis of the required logic elements to implement the decoder, on various platforms, is presented

Application of a finite-state model to text compression

The bit-oriented finite-state model applied in Dynamic Markov Compression (DMC [5]) is here generalized to a larger alphabet. The finite-state machine is built adaptively during compression, by applying two tyes of modifications to the machine structure: state cloning and shortcut creation. The machine size is kept tolerable by an escape transition mechanism. Similar to DMC, the new method is combined with arithmetic coding, based on the maintained transition frequencies. The experiments show that the new approach produces notably better compression gains for different sorts of texts in natural and formal languages. In some cases the results are better than for any compression technique found in the literature

Piecewise arithmetic coding

A new coding technique, FIXARI, is easily programmed to produce fixed-length codewords quickly for partial decoding and indexing. Errors in transmission (bit switches) remain local to the keyboard.<<ETX>>

Minimal space, average linear time duplicate deletion

The common method used to delete duplicates in a file is to sort the records. Duplicates may then be deleted either on-the-fly or in a second pass. Here, we present a new method based on hashing. Multiple passes are made over the file and detected duplicates move in place to the tail end of the file. The algorithm requires, on the average, only linear time and works with 0(1) extra space

Text compression using prediction

In the compression of the text files, the dependencies between the successive characters should be exploited to as great an extent as possible. There are two obvious possibilities: either to detect and encode often occurring character strings, or to encode successors of character blocks. This paper presents two methods based on the latter approach. In the first method we encode only the most probable successors of blocks, whereas in the second we encode them all, using the knowledge of their distribution. The second method uses recursion to store effectively the dependencies between the characters and this results in good compression gains in practical cases.