Cell-Based 2-Step Scalar Deadzone Quantization for JPEG2000

Bartrina-Rapesta, Joan; Aulí-Llinàs, Francesc; Serra-Sagristà, Joan

doi:10.1109/dcc.2014.51

Cited by 2 publications

(1 citation statement)

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“…These coefficients value is then needed to be expressed in less bit which lead to rounding error. These approximated value is quantized thus creating a lossy compressed imaged [3].…”

Section: Introductionmentioning

confidence: 99%

Minimizing median difference quantization error for image compression

Taujuddin

Ibrahim

Sari

2016

2016 International Conference on Information and Communication Technology Convergence (ICTC)

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Abstract-This research emphasis on problem of quantization process in transform-based image compression. In specifying the quantizer, the size of interval is giving huge impact to the quantization performance. Generally, high quantization error will occurred if large interval is used at high difference value bin. Thus, quantizer needs to be design carefully to outfit the value as efficient as possible to reduce the quantization error. Contrary to the traditional approach, that apply uniform or non-uniform quantization step size, the proposed one utilize the high occurrence of zero coefficient by re-allocate the non-zero coefficient in a group for quantization. Then the proposed coder employ quantization error minimization mechanism by calculating the difference median quantization error at each quantization interval class. The results are then compared to the standard transform based algorithm and we found that the proposed algorithm compress the image effectively without harming the quality of the compressed image.

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“…These coefficients value is then needed to be expressed in less bit which lead to rounding error. These approximated value is quantized thus creating a lossy compressed imaged [3].…”

Section: Introductionmentioning

confidence: 99%