We provide a tutorial on arithmetic coding, showing how it provides nearly optimal data compression and how it can be matched with almost any probabilistic model. We indicate the main disadvantage of arithmetic coding, its slowness, and give the basis of a fast, space-efficient, approximate arithmetic coder with only minimal loss of compression efficiency. Our coder is based on the replacement of arithmetic by table lookups coupled with a new deterministic probability estimation scheme.

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The problem of scalar quantization of certain memoryless sources with entropy coding is considered. The work is divided into two parts. In the first

There is a tradeoff between the speed of a data compressor and the level of compression it can achieve. Improving compression generally requires more computation; and improving speed generally sacrifices compression. In this thesis, we examine a range of tradeoffs for text and video. In text compression, we attempt to bridge the gap between statistical techniques, which exhibit a greater amount of compression but are computationally intensive, and dictionary-based techniques, which give less compression but run faster. We combine the context modeling of statistical coding with dynamic dictionaries into a hybrid coding scheme we call Dictionary by Partial Matching. In low-bit-rate video compression, we explore the speed-compression tradeoffs with a range of motion estimation techniques operating within the H.261 video coding standard. We initially consider algorithms that explicitly minimizes bit rate and combination of rate and distortion. With insights gained from the explicit minimization algorithms, we propose a new technique for motion estimation that minimizes an efficiently computed heuristic function. The new technique gives compression efficiency comparable to the explicit-minimization algorithms while running much faster. We also explore bit-minimization in a non-standard quadtree-based video coder that codes

This work concerns the search for text compressors that compress better than existing dictionary coders, but run faster than statistical coders. We describe a new method for text compression using multiple dictionaries, one for each context of preceeding characters, where the contexts have varying lengths. The context to be used is determined using an escape mechanism similar to that of PPM methods. We describe modifications of three popular dictionary coders along these lines and experiments evaluating their effectiveness using the text files in the Calgary corpus. Our results suggest that modifying LZ77, LZFG, and LZW along these lines yields improvements in compression of about 3%, 6%, and 15%, respectively.

Wireless communication systems are currently being rapidly developed due to strong market demands. Unlike wireline communication, the bandwidth is very limited and is unlikely to grow significantly in the future. Hence it is necessary for both the receiving and sending entities to employ complex computations to increase the effective bandwidth of communication channels. One way to increase the effective bandwidth of a communication channel is to reduce the amount of data to be injected into the channel using data compression. Yet to provide reliable, end-to-end service, data compression needs to be lossless, adaptive, and transparent to the user. The throughput of data compression should also be in tune with the overall throughput of the system (10 Mbps - 100 Mbps) while minimizing communication delay. Since data compression and decompression need to be integrated into a compact and affordable wireless system, it is crucial that the implementation is minimized in terms of both area and...

� Arithmetic coding is slow in general: To decode a symbol, we need a series of decisions and multiplications: While (Tag> LOW + RANGE * Sum(n) / N- 1) { n++; � The complexity is greatly reduced if we have only two symbols: 0 and 1. symbol 0 symbol 1 0 x 1 � Only two intervals: [0, x), [x, 1)

Arithmetic coding provides an effective mechanism for removing redundancy in the encoding of data. In this paper we describe a VLSI architecture of an arithmetic coder for a multi-level alphabet (256 symbols), that includes the storing and updating of probabilities, the updating of the interval and the correction of the codeword. The architecture is based on the utilization of redundant arithmetic and the development of new schemes for storing and updating the cumulative probabilities and updating the range and left point of the current interval. The proposed implementation is compared with one that does not include these improvements and shown to result in a significantly lower complexity and shorter cycle. 1 INTRODUCTION Arithmetic coding [4] [21] [12] is an efficient method for removing redundancy in the encoding of data, obtaining a larger compression ratio than the traditional and well--known Huffman coding method. It actually achieves the theoretical entropy bound to com...

: A two-stage modelling schema to be used together with arithmetic coding is proposed. Main motivation of the work has been the relatively slow operation of arithmetic coding. The new modelling schema reduces the use of arithmetic coding by applying to large white regions global modelling which consumes less time. This composite method works well and with a set of test images it took only ca. 41% of time required by QM-coder. At the same time the loss in compression ratio is only marginal. Index terms: Image compression, arithmetic coding, block coding, modelling. 1. Introduction Pictorial information is expressed by a very simple model in black-and-white images. Only two colours, black and white, are recognised and even the greyness of different picture elements is omitted so that the image consists of a configuration of pixels each representing the pure black or white colour. In spite of the binary nature of the image files they have a high demand of the storage space. This brings ma...

and microfilm, without permission in writing from the ITU. Contents Page Introduction.............................................................................................................................................................. iii 1 Scope ............................................................................................................................................................ 1 2 Normative references..................................................................................................................................... 1 3 Definitions, abbreviations and symbols ......................................................................................................... 1 4 General ......................................................................................................................................................... 12 5 Interchange format requirements ...........................................................................