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 압축을 위한 영상 분할 기반의 적응적 예측 코딩 방법을 제안한다. MAR(Multiplicative Autoregressive) 예측 코딩은 효율적인 무손실 압축 방식입니다. MAR 모델의 예측자는 로컬 이미지 처리로 인해 로컬 이미지 통계의 변화에 적응할 수 있습니다. 그러나 블록 단위로 분할된 영상 내에서 지역적 통계량이 변화하는 영상에 적용하면 MAR 방식의 성능이 저하된다. 또한, 예측 계수와 같은 부가 정보는 각 블록과 함께 디코더에 전송되어야 합니다. 압축 성능을 향상시키기 위해 영상 분할을 이용한 MAR 코딩 방법을 개선한다. 제안된 MAR 예측기는 각 픽셀에서 이미지의 로컬 통계에 효율적으로 적용할 수 있습니다. 또한 기존 MAR 방법에 비해 전송해야 하는 부가 정보가 적습니다.
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부
Takayuki NAKACHI, Tatsuya FUJII, Junji SUZUKI, "Pel Adaptive Predictive Coding Based on Image Segmentation for Lossless Compression" in IEICE TRANSACTIONS on Fundamentals,
vol. E82-A, no. 6, pp. 1037-1046, June 1999, doi: .
Abstract: In this paper, we propose an adaptive predictive coding method based on image segmentation for lossless compression. MAR (Multiplicative Autoregressive) predictive coding is an efficient lossless compression scheme. Predictors of the MAR model can be adapted to changes in the local image statistics due to its local image processing. However, the performance of the MAR method is reduced when applied to images whose local statistics change within the block-by-block subdivided image. Furthermore, side-information such as prediction coefficients must be transmitted to the decoder with each block. In order to enhance the compression performance, we improve the MAR coding method by using image segmentation. The proposed MAR predictor can be adapted to the local statistics of the image efficiently at each pixel. Furthermore, less side-information need be transmitted compared with the conventional MAR method.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e82-a_6_1037/_p
부
@ARTICLE{e82-a_6_1037,
author={Takayuki NAKACHI, Tatsuya FUJII, Junji SUZUKI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Pel Adaptive Predictive Coding Based on Image Segmentation for Lossless Compression},
year={1999},
volume={E82-A},
number={6},
pages={1037-1046},
abstract={In this paper, we propose an adaptive predictive coding method based on image segmentation for lossless compression. MAR (Multiplicative Autoregressive) predictive coding is an efficient lossless compression scheme. Predictors of the MAR model can be adapted to changes in the local image statistics due to its local image processing. However, the performance of the MAR method is reduced when applied to images whose local statistics change within the block-by-block subdivided image. Furthermore, side-information such as prediction coefficients must be transmitted to the decoder with each block. In order to enhance the compression performance, we improve the MAR coding method by using image segmentation. The proposed MAR predictor can be adapted to the local statistics of the image efficiently at each pixel. Furthermore, less side-information need be transmitted compared with the conventional MAR method.},
keywords={},
doi={},
ISSN={},
month={June},}
부
TY - JOUR
TI - Pel Adaptive Predictive Coding Based on Image Segmentation for Lossless Compression
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1037
EP - 1046
AU - Takayuki NAKACHI
AU - Tatsuya FUJII
AU - Junji SUZUKI
PY - 1999
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E82-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 1999
AB - In this paper, we propose an adaptive predictive coding method based on image segmentation for lossless compression. MAR (Multiplicative Autoregressive) predictive coding is an efficient lossless compression scheme. Predictors of the MAR model can be adapted to changes in the local image statistics due to its local image processing. However, the performance of the MAR method is reduced when applied to images whose local statistics change within the block-by-block subdivided image. Furthermore, side-information such as prediction coefficients must be transmitted to the decoder with each block. In order to enhance the compression performance, we improve the MAR coding method by using image segmentation. The proposed MAR predictor can be adapted to the local statistics of the image efficiently at each pixel. Furthermore, less side-information need be transmitted compared with the conventional MAR method.
ER -