Image Compression Using Polynomial Fitting

Othman, Shaimaa; Mohamed, Amr E.; Nossair, Zaki B.; Eladawy, Mohamed

doi:10.1109/iceca.2019.8822232

Cited by 4 publications

(1 citation statement)

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“…This paper aims to present a novel solution to effectively mitigate the Mura problem in the early shipping stage while minimizing production costs. We propose a machine learning-based method that leverages surface fitting [2] and vector quantization [3] to compress De-Mura compensation data. Remarkable compression ratios of up to 8 are achieved through vector quantization, providing significant economic benefits to manufacturers.…”

Section: Introductionmentioning

confidence: 99%

P‐57: A Novel Compression Algorithm using Machine Learning for Mura Compensation of OLED Panel

Son,

Kim,

Shin

et al. 2024

Symp Digest of Tech Papers

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In the modern display panel manufacturing industry, the Mura problem that occurs during initial shipment has a significant impact on improving image quality. The purpose of this paper is to effectively solve these Mura defects while reducing additional production costs. We propose an innovative method of storing and compressing the De‐Mura compensation data of the display panel in external memory. Two‐dimensional compensation data compression based on machine learning using surface fitting and vector quantization is presented as an effective response to the Mura problem. This approach, which achieves up to 8:1 compression ratio while minimizing image quality degradation through vector quantization, is expected to provide economic benefits to manufacturers. The paper also introduces a new surface prediction method that replaces the existing DC‐shift and uses residual vector quantization, which can reduce the implementation complexity of vector quantization. Through experimental results, this study demonstrates that the compensated data compression technique yields excellent results in post‐compression restoration performance.

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Section: Introductionmentioning

confidence: 99%