Quantization Error Minimization by Reducing Median Difference at Quantization Interval Class

N. S. A. M. Taujuddin, Rosziati Ibrahim, Suhaila Sari


In this paper, a new technique to define the size of quantization interval is defined. In general, high quantization error will occur if large interval is used at a large difference value class whereas low quantization error will occur if a small interval is used at a large difference value class. However, the existence of too many class intervals will lead to a higher system complexity. Thus, this research is mainly about designing a quantization algorithm that can provide an efficient interval as possible to reduce the quantization error. The novelty of the proposed algorithm is to utilize the high occurrence of zero coefficient by re-allocating the non-zero coefficient in a group for quantization. From the experimental results provided, this new algorithm is able to produce a high compressed image without compromising with the image quality.


Error Minimization; Quantization; Interval Class;

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N. S. A. M. Taujuddin and R. Ibrahim, “Medical Image Compression by using Threshold Predicting Wavelet-Based Algorithm,” Lect. Notes Electrical. Eng, Advance Computer and Communication. Technology Springer, vol. 315, pp. 755–765, 2014.

H. Kobayashi, M. Iwahashi, and H. Kiya, “Weighted Median Cut Quantization and its Applications,” IEEE Int. Symp. Intell. Signal Process. Commun. Syst., no. Ispacs, pp. 509–514, 2012.

M. Iwahashi, H. Kobayashi, and H. Kiya, “Lossy compression of sparse histogram image,” 2012 IEEE Int. Conf. Acoust. Speech Signal Process., pp. 1361–1364, 2012.

J. B. Rapesta, F. A. L. I. Blanes, and J. S. Sagrista, “Cell-Based 2-Step Scalar Deadzone Quantization for JPEG2000,” 2014 Data Compression Conf., pp. 143–152, 2014.

J. Sun, Y. Duan, J. Li, J. Liu, and Z. Guo, “Rate-distortion analysis of dead-zone plus uniform threshold scalar quantization and its application - Part II: Two-pass VBR coding for H.264/AVC,” IEEE Trans. Image Process., vol. 22, no. 1, pp. 215–228, 2013.

S. M. Hosseini and A.-R. Naghsh-Nilchi, “Medical ultrasound image compression using contextual vector quantization.,” Comput. Biol. Med., vol. 42, no. 7, pp. 743–50, Jul. 2012.

H. Jiang, Z. Ma, Y. Hu, B. Yang, and L. Zhang, “Medical image compression based on vector quantization with variable block sizes in wavelet domain,” Comput. Intell. Neurosci., vol. 2012, 2012.

M. S. Savic, Z. H. Peric, and N. Simic, “Expert Systems with Applications Coding algorithm for grayscale images based on Linear Prediction and dual mode quantization,” Expert Systems With Applications, vol. 42, pp. 7285–7291, 2015.

J. Bartrina-rapesta and F. Aulí-llinàs, “Cell-Based Two-Step Scalar Deadzone Quantization for High Bit-Depth Hyperspectral Image Coding,” IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 9, pp. 1893–1897, 2015.

J. Abirami, K. N. S. Sivasankari, and S. Ramya, “Performance analysis of image compression using wavelet thresholding,” IEEE Conf. Inf. Commun. Technol., no. ICT, pp. 194–198, 2013.

A. Mulla, J. Baviskar, P. Borra, S. Yadav, and A. Baviskar, “Energy thresholding based sub-band elimination DWT scheme for image compression,” Proc. - 2015 Int. Conf. Commun. Inf. Comput. Technol. ICCICT 2015, 2015.

H. Rekha and P. Samundiswary, “Image compression using multilevel thresholding based Absolute Moment Block Truncation Coding for WSN,” Proc. 2016 IEEE Int. Conf. Wirel. Commun. Signal Process. Networking, WiSPNET 2016, pp. 396–400, 2016.

S. A. Lashari, R. Ibrahim, and N. Senan, “Wavelet Threshold DeNoising for Mammogram Images,” Int. J. Softw. Eng. Its Appl., vol. 9, no. 6, pp. 215–226, 2015.


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