The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Neste artigo, investigamos preditores de erro absoluto médio mínimo (mmae) para codificação de imagens sem perdas. Em alguns sistemas de codificação de imagens sem perdas baseados em predição, o desempenho da codificação depende em grande parte da eficiência dos preditores. Nesse caso, preditores de erro quadrático médio mínimo (mmse) são frequentemente usados. De modo geral, esses preditores têm um problema que discrepantes partindo muito longe de uma linha de regressão são visíveis o suficiente para obscurecer internos. Ou seja, na compressão de imagens, grandes erros de predição próximos às bordas causam a degradação da precisão da predição de áreas planas. Por outro lado, os preditores mmae são menos sensível às bordas e fornecem previsões mais precisas para áreas planas do que os preditores mmse. Ao mesmo tempo, a precisão da previsão das áreas de borda é reduzida. No entanto, a entropia dos erros de predição baseados em preditores mmae é reduzida em comparação com a dos preditores mmse porque as imagens gerais consistem principalmente em áreas planas. Neste estudo, adotamos os modelos de função Laplaciano e Gaussiano para erros de predição baseados em preditores mmae e mmse, respectivamente, e mostramos que os preditores mmae superam os preditores convencionais baseados em mmse, incluindo pesada preditores mmse em termos de desempenho de codificação.
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Yoshihiko HASHIDUME, Yoshitaka MORIKAWA, Shuichi MAKI, "Minimum Mean Absolute Error Predictors for Lossless Image Coding" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 6, pp. 1783-1792, June 2008, doi: 10.1093/ietisy/e91-d.6.1783.
Abstract: In this paper, we investigate minimum mean absolute error (mmae) predictors for lossless image coding. In some prediction-based lossless image coding systems, coding performance depends largely on the efficiency of predictors. In this case, minimum mean square error (mmse) predictors are often used. Generally speaking, these predictors have a problem that outliers departing very far from a regression line are conspicuous enough to obscure inliers. That is, in image compression, large prediction errors near edges cause the degradation of the prediction accuracy of flat areas. On the other hand, mmae predictors are less sensitive to edges and provide more accurate prediction for flat areas than mmse predictors. At the same time, the prediction accuracy of edge areas is brought down. However, the entropy of the prediction errors based on mmae predictors is reduced compared with that of mmse predictors because general images mainly consist of flat areas. In this study, we adopt the Laplacian and the Gaussian function models for prediction errors based on mmae and mmse predictors, respectively, and show that mmae predictors outperform conventional mmse-based predictors including weighted mmse predictors in terms of coding performance.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.6.1783/_p
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@ARTICLE{e91-d_6_1783,
author={Yoshihiko HASHIDUME, Yoshitaka MORIKAWA, Shuichi MAKI, },
journal={IEICE TRANSACTIONS on Information},
title={Minimum Mean Absolute Error Predictors for Lossless Image Coding},
year={2008},
volume={E91-D},
number={6},
pages={1783-1792},
abstract={In this paper, we investigate minimum mean absolute error (mmae) predictors for lossless image coding. In some prediction-based lossless image coding systems, coding performance depends largely on the efficiency of predictors. In this case, minimum mean square error (mmse) predictors are often used. Generally speaking, these predictors have a problem that outliers departing very far from a regression line are conspicuous enough to obscure inliers. That is, in image compression, large prediction errors near edges cause the degradation of the prediction accuracy of flat areas. On the other hand, mmae predictors are less sensitive to edges and provide more accurate prediction for flat areas than mmse predictors. At the same time, the prediction accuracy of edge areas is brought down. However, the entropy of the prediction errors based on mmae predictors is reduced compared with that of mmse predictors because general images mainly consist of flat areas. In this study, we adopt the Laplacian and the Gaussian function models for prediction errors based on mmae and mmse predictors, respectively, and show that mmae predictors outperform conventional mmse-based predictors including weighted mmse predictors in terms of coding performance.},
keywords={},
doi={10.1093/ietisy/e91-d.6.1783},
ISSN={1745-1361},
month={June},}
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TY - JOUR
TI - Minimum Mean Absolute Error Predictors for Lossless Image Coding
T2 - IEICE TRANSACTIONS on Information
SP - 1783
EP - 1792
AU - Yoshihiko HASHIDUME
AU - Yoshitaka MORIKAWA
AU - Shuichi MAKI
PY - 2008
DO - 10.1093/ietisy/e91-d.6.1783
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2008
AB - In this paper, we investigate minimum mean absolute error (mmae) predictors for lossless image coding. In some prediction-based lossless image coding systems, coding performance depends largely on the efficiency of predictors. In this case, minimum mean square error (mmse) predictors are often used. Generally speaking, these predictors have a problem that outliers departing very far from a regression line are conspicuous enough to obscure inliers. That is, in image compression, large prediction errors near edges cause the degradation of the prediction accuracy of flat areas. On the other hand, mmae predictors are less sensitive to edges and provide more accurate prediction for flat areas than mmse predictors. At the same time, the prediction accuracy of edge areas is brought down. However, the entropy of the prediction errors based on mmae predictors is reduced compared with that of mmse predictors because general images mainly consist of flat areas. In this study, we adopt the Laplacian and the Gaussian function models for prediction errors based on mmae and mmse predictors, respectively, and show that mmae predictors outperform conventional mmse-based predictors including weighted mmse predictors in terms of coding performance.
ER -