An effective method for still image compression/decompression for transmission on PSTN lines based on modifications of Huffman coding

Zolghadr-E-Asli, Alireza; Alipour, Siamak

doi:10.1016/j.compeleceng.2003.01.001

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…Lossless compression allows decreasing of average bit-rate without losing information and can be achieved using an entropy coding [4]. There are many different types of entropy codes, examples are Huffman [2,3,21], arithmetic and Golomb-Rice. Golomb-Rice code is especially suitable for data with Laplacian distribution since it gives average bit-rate very close to the entropy and it is much simpler for implementation than Huffman code [9,16,17].…”

Section: Introductionmentioning

confidence: 99%

Design of a Hybrid Quantizer with Variable Length Code

Perić

Dincic

Petković

2010

Fundamenta Informaticae

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In this paper a new model for compression of Laplacian source is given. This model consists of hybrid quantizer whose output levels are coded with Golomb-Rice code. Hybrid quantizer is combination of uniform and nonuniform quantizer, and it can be considered as generalized quantizer, whose special cases are uniform and nonuniform quantizers. We propose new generalized optimal compression function for companding quantizers. Hybrid quantizer has better performances (smaller bit-rate and complexity for the same quality) than both uniform and nonuniform quantizers, because it joins their good characteristics. Also, hybrid quantizer allows great flexibility, because there are many combinations of number of levels in uniform part and in nonuniform part, which give similar quality. Each of these combinations has different bit-rate and complexity, so we have freedom to choose combination which is the most appropriate for our application, in regard to quality, bit-rate and complexity. We do not have such freedom of choice when we use uniform or nonuniform quantizers. Until now, it has been thought that uniform quantizer is the most appropriate to use with lossless code, but in this paper we show that combination of hybrid quantizer and lossless code gives better performances. As lossless code we use Golomb-Rice code because it is especially suitable for Laplacian source since it gives average bit-rate very close to the entropy and it is easier for implementation than Huffman code. Golomb-Rice code is used in many modern compression standards. Our model can be used for compression of all signals with Laplacian distribution.

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Section: Introductionmentioning

confidence: 99%

Design of a Hybrid Quantizer with Variable Length Code

Perić

Dincic

Petković

2010

Fundamenta Informaticae

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“…VQ is a lossy technique whereas the other two are lossless. This paper presents an analysis of Arithmetic [10][11][12][13] and Huffman [14][15][16] based variablelength coding for compression.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Arithmetic and Huffman Compression Techniques by Using DWT-DCT

Kumar¹,

Kumar²

2021

IJIGSP

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In the recent era, digital contents are exchanging over the internet and it has increased exponentially. Sometimes, we need small sizes to share the real world, because of narrow bandwidth. Hence, the data compression concept came in limelight to utilize the storage capacity and available bandwidth efficiently. This paper presents an analysis of Arithmetic and Huffman compression techniques based on a hybrid combination of the DWT-DCT techniques. The input image is decomposed up to the 3 rd level by using the DWT and then Arithmetic & Huffman coding is applied separately on quantized sub-bands on 2 nd as well as 3 rd level coefficients from approximation subbands to get a high compression ratio and high peak signal-to-noise ratio values. On the third level approximation subband, the DCT method is applied to reduce the blocking effect. Simulation results show that the Arithmetic coding exhibits higher CR than Huffman coding, but smaller PSNR values.

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“…Lossless compression allows decreasing of bit rate without losing information and can be achieved by using some of the entropy codes [1,2,4,5]. There are many different types of entropy codes, the examples of which are Huffman, Golomb-Rice and arithmetic code [3,[6][7][8][9][10][11]. In many modern applications, a combination of a quantiser and a lossless coder is used.…”

Section: Introductionmentioning

confidence: 99%