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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 무손실 이미지 코딩을 위한 최소 평균 절대 오차(mmae) 예측 변수를 조사합니다. 일부 예측 기반 무손실 이미지 코딩 시스템에서 코딩 성능은 주로 예측기의 효율성에 따라 달라집니다. 이 경우 최소 평균 제곱 오차(mmse) 예측 변수가 자주 사용됩니다. 일반적으로 이러한 예측 변수에는 다음과 같은 문제가 있습니다. 이상치 회귀선에서 아주 멀리 벗어나면 눈에 잘 띄지 않습니다. 인라이어. 즉, 영상 압축 시 가장자리 부근의 예측 오차가 크면 평탄한 영역의 예측 정확도가 저하됩니다. 반면 mmae 예측 변수는 다음과 같습니다. 덜 민감한 mmse 예측자보다 평평한 영역에 대해 더 정확한 예측을 제공합니다. 동시에 가장자리 영역의 예측 정확도도 낮아집니다. 그러나 mmae 예측자를 기반으로 한 예측 오차의 엔트로피는 일반 영상이 주로 평평한 영역으로 구성되어 있기 때문에 mmse 예측자에 비해 감소합니다. 본 연구에서는 각각 mmae 및 mmse 예측자를 기반으로 한 예측 오류에 대해 Laplacian 및 Gaussian 함수 모델을 채택하고 mmae 예측자가 다음을 포함한 기존 mmse 기반 예측자보다 성능이 우수함을 보여줍니다. 가중 코딩 성능 측면에서 mmse 예측 변수.
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부
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
부
@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},}
부
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 -