Local-adaptive blocks-based predictor for lossless image compression

Novikov, Dmitriy; Egorov, Nickolay; Gilmutdinov, Marat

doi:10.1109/red.2016.7779337

Cited by 5 publications

(2 citation statements)

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“…In this section, detail about the experimental setup, dataset, and results are discussed. The performance of the proposed method is also compared with stateof-the-art predictors, i.e., MED (Fouad, 2015), GAP (Tiwari and Kumar, 2008), FLIF (Sneyers and Wuille, 2016), and LBP (Novikov et al, 2016).…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The value of entropy, bpp, and computational running time in second(s) of the prediction process is calculated to evaluate the iMED predictor performance. The proposed method results demonstrate significant improvement in entropy value, bpp, and computational running time(s) compared to state-of-the-art MED (Fouad, 2015), GAP (Tiwari and Kumar, 2008), FLIF (Sneyers and Wuille, 2016), and LBP (Novikov et al, 2016) predictors.…”

Section: This Paper Proposes a New Improved Medianmentioning

confidence: 93%

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Improved Median Edge Detection (iMED) for Lossless Image Compression

et al. 2023

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A wide variety of applications are used in lossless image compression models, especially in medical, space, and aerial imaging domains. Predictive coding improves the performance of lossless image compression, which highly relies on entropy error. Lower entropy error results in better image compression. The main focus of this research is to improve the prediction process by minimizing the entropy error. This paper proposes a novel idea for improved Median Edge Detection (iMED) predictor for lossless image compression. MED predictor is improved using k-means clustering and finding the local context of pixels using 20-Dimensional Difference (DDx20) for input images and updates the cluster weights using learning rates (µi) to minimize the prediction errors of pixels. The performance of the proposed predictor is evaluated on the standard grey-scale test images dataset and KODAK image dataset. Results are obtained and compared based on entropy error, bits per pixel (bpp), and computational running time in seconds(s) with the MED, GAP, FLIF, and LBP predictors. The performance of the proposed iMED predictor improves significantly in terms of the entropy error, bpp, and computational running time in seconds(s) after comparison with different state-of-the-art predictors.

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Section: Experiments and Resultsmentioning

confidence: 99%