Image Coder using Ant Tree Vector Quantization Algorithm

Channa, A.H.; Hussain, Shariq

doi:10.1109/inmic.2005.334471

Cited by 2 publications

(1 citation statement)

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“…Transformation a signal is just another form of representing this signal, it does not change the information content present in it (Misiti, et al, 2003). Wavelet transform is used to provide a multi-resolution image representation fitting human visual system which has excellent energy compaction property suitable to exploit redundancy in an image to achieve compression (Channa & Hussain, 2005). Quantization is a necessary component in lossy coding and has direct impact on the bit rate (Shi & Sun, 2008).…”

Section: Introductionmentioning

confidence: 99%

Enhanced Image Coding Scheme Based on Modified Embedded Zerotree Wavelet Transform (DMEZW)

Waysi¹,

Brifcani

2017

SJUOZ

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ABSTRACT:In this paper the proposed scheme uses different processing methods by applying Integer Lifting Wavelet Transform (ILWT) on gray scale image generating four subband is presented. The low frequency subbands is compressed losslessly by the Developed Modified Embedded Zerotree Wavelet Transform (DMEZW) directly. The high and middle frequency subbands are compressed lossyly by applying first to single stage Vector Quantization (VQ) then to DMEZW, finally generating two vectors ready for entropy coding and it is presented as Arithmetic Coding (AC) to produce a bit stream to be stored or transmitted. The main improvements of DMEZW is done by modifying the scanning strategy of the wavelet coefficients and the quantization threshold. The high and low frequency subbands are manipulated separately. The experimental results show that the developed method can improve the quality of the recovered image and the encoding efficiency. The proposed scheme programming code has achieved high Compression Ratio (CR) and remarkable Peak Signal to Noise Ratio (PSNR).

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Section: Introductionmentioning

confidence: 99%