Improved thresholding and quantization techniques for image compression
In recent decades, digital images have become increasingly important. With many modern applications use image graphics extensively, it tends to burden both the storage and transmission process. Despite the technological advances in storage and transmission, the demands placed on storage and bandwidt...
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| 格式: | Thesis |
| 语言: | English English English |
| 出版: |
2017
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| 主题: | |
| 在线阅读: | http://eprints.uthm.edu.my/347/1/24p%20NIK%20SHAHIDAH%20AFIFI%20MD%20TAUJUDDIN.pdf http://eprints.uthm.edu.my/347/2/NIK%20SHAHIDAH%20AFIFI%20MD%20TAUJUDDIN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/347/3/NIK%20SHAHIDAH%20AFIFI%20MD%20TAUJUDDIN%20WATERMARK.pdf |
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| 总结: | In recent decades, digital images have become increasingly important. With many modern applications use image graphics extensively, it tends to burden both the storage and transmission process. Despite the technological advances in storage and transmission, the demands placed on storage and bandwidth capacities still exceeded its availability. Moreover, the compression process involves eliminating some data that degrades the image quality. Therefore, to overcome this problem, an improved thresholding and quantization techniques for image compression is proposed. Firstly, the generated wavelet coefficients obtained from the Discrete Wavelet Transform (DWT) process are thresholded by the proposed Standard Deviation-Based Wavelet Coefficients Threshold Estimation Algorithm. The proposed algorithm estimates the best threshold value at each detail subbands. This algorithm exploits the huge number of near-zero coefficients exist in detail subbands. For different images, the distribution of wavelet coefficients at each subband are substantially different. So, by calculating the standard deviation value of each subband, a better threshold value can be obtained. Next, the retained wavelet coefficients are subjected to the next proposed Minimizing Median Quantization Error Algorithm. The proposed algorithm utilizes the high occurrence of zero coefficient obtained by the previous thresholding process by re-allocating the zero and non-zero coefficients in different groups for quantization. Then, quantization error minimization mechanism is employed by calculating the median quantization error at each quantization interval class. The results are then compared to the existing algorithms and it is found that the proposed compression algorithm shows double increase in compression ratio performance, produces higher image quality with PSNR value above 40dB and ensures a better bit saving with smooth control at bit rate higher than 4 bpp. Thus, the proposed algorithm provides an alternative technique to compress the digital image. |
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