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
Bunyi sangat merendahkan kualiti imej dan prestasi algoritma pemampatan imej. Kertas kerja ini membentangkan pendekatan untuk perwakilan dan pemampatan imej sintetik yang bising. Konsep baru ramalan berasaskan wilayah (RBP) pertama kali diperkenalkan, dan kemudian model RBP digunakan pada imej bising. Dalam teknik pengekodan ramalan konvensional, konteks untuk ramalan sentiasa terdiri daripada piksel individu yang mengelilingi piksel untuk diproses. Model RBP menggunakan kawasan dan bukannya piksel individu sebagai konteks untuk ramalan. Algoritma untuk pelaksanaan RBP dicadangkan dan digunakan pada imej sintetik yang bising dalam eksperimen kami. Menggunakan RBP untuk mencari data sisa dan mengekodnya, kami mencapai kadar bit 1.10 bit/piksel untuk imej sintetik yang bising. Imej nyahmampat mencapai SNR puncak 42.59 dB. Berbanding dengan SNR puncak 41.01 dB untuk imej sintetik yang bising, kualiti imej sintetik nyahmampat dipertingkatkan sebanyak 1.58 dB dalam pengertian MSE. Berbeza dengan cadangan algoritma pemampatan kami dengan peningkatan dalam kualiti imej, kaedah pengekodan konvensional boleh memampatkan data imej hanya dengan mengorbankan kualiti imej yang lebih rendah. Pada kadar bit yang sama, standard pemampatan imej JPEG menyediakan SNR puncak 33.17 dB untuk imej sintetik yang bising, dan penapis median konvensional dengan 3
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Salinan
Yu LIU, Masayuki NAKAJIMA, "Region-Based Prediction Coding for Compression of Noisy Synthetic Images" in IEICE TRANSACTIONS on Information,
vol. E82-D, no. 2, pp. 461-467, February 1999, doi: .
Abstract: Noise greatly degrades the image quality and performance of image compression algorithms. This paper presents an approach for the representation and compression of noisy synthetic images. A new concept region-based prediction (RBP) model is first introduced, and then the RBP model is utilized on noisy images. In the conventional predictive coding techniques, the context for prediction is always composed of individual pixels surrounding the pixel to be processed. The RBP model uses regions instead of individual pixels as the context for prediction. An algorithm for the implementation of RBP is proposed and applied to noisy synthetic images in our experiments. Using RBP to find the residual data and encoding them, we achieve a bit rate of 1.10 bits/pixel for the noisy synthetic image. The decompressed image achieves a peak SNR of 42.59 dB. Compared with a peak SNR of 41.01 dB for the noisy synthetic image, the quality of the decompressed synthetic image is improved by 1.58 dB in the MSE sense. In contrast to our proposed compression algorithm with its improvement in image quality, conventional coding methods can compress image data only at the expense of lower image quality. At the same bit rate, the image compression standard JPEG provides a peak SNR of 33.17 dB for the noisy synthetic image, and the conventional median filter with a 3
URL: https://global.ieice.org/en_transactions/information/10.1587/e82-d_2_461/_p
Salinan
@ARTICLE{e82-d_2_461,
author={Yu LIU, Masayuki NAKAJIMA, },
journal={IEICE TRANSACTIONS on Information},
title={Region-Based Prediction Coding for Compression of Noisy Synthetic Images},
year={1999},
volume={E82-D},
number={2},
pages={461-467},
abstract={Noise greatly degrades the image quality and performance of image compression algorithms. This paper presents an approach for the representation and compression of noisy synthetic images. A new concept region-based prediction (RBP) model is first introduced, and then the RBP model is utilized on noisy images. In the conventional predictive coding techniques, the context for prediction is always composed of individual pixels surrounding the pixel to be processed. The RBP model uses regions instead of individual pixels as the context for prediction. An algorithm for the implementation of RBP is proposed and applied to noisy synthetic images in our experiments. Using RBP to find the residual data and encoding them, we achieve a bit rate of 1.10 bits/pixel for the noisy synthetic image. The decompressed image achieves a peak SNR of 42.59 dB. Compared with a peak SNR of 41.01 dB for the noisy synthetic image, the quality of the decompressed synthetic image is improved by 1.58 dB in the MSE sense. In contrast to our proposed compression algorithm with its improvement in image quality, conventional coding methods can compress image data only at the expense of lower image quality. At the same bit rate, the image compression standard JPEG provides a peak SNR of 33.17 dB for the noisy synthetic image, and the conventional median filter with a 3
keywords={},
doi={},
ISSN={},
month={February},}
Salinan
TY - JOUR
TI - Region-Based Prediction Coding for Compression of Noisy Synthetic Images
T2 - IEICE TRANSACTIONS on Information
SP - 461
EP - 467
AU - Yu LIU
AU - Masayuki NAKAJIMA
PY - 1999
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E82-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 1999
AB - Noise greatly degrades the image quality and performance of image compression algorithms. This paper presents an approach for the representation and compression of noisy synthetic images. A new concept region-based prediction (RBP) model is first introduced, and then the RBP model is utilized on noisy images. In the conventional predictive coding techniques, the context for prediction is always composed of individual pixels surrounding the pixel to be processed. The RBP model uses regions instead of individual pixels as the context for prediction. An algorithm for the implementation of RBP is proposed and applied to noisy synthetic images in our experiments. Using RBP to find the residual data and encoding them, we achieve a bit rate of 1.10 bits/pixel for the noisy synthetic image. The decompressed image achieves a peak SNR of 42.59 dB. Compared with a peak SNR of 41.01 dB for the noisy synthetic image, the quality of the decompressed synthetic image is improved by 1.58 dB in the MSE sense. In contrast to our proposed compression algorithm with its improvement in image quality, conventional coding methods can compress image data only at the expense of lower image quality. At the same bit rate, the image compression standard JPEG provides a peak SNR of 33.17 dB for the noisy synthetic image, and the conventional median filter with a 3
ER -