Estimating the Quality of Fractal Compressed Images Using Lacunarity
Summary :
In this paper, we propose a highly accurate method for estimating the quality of images compressed using fractal image compression. Using an iterated function system, fractal image compression compresses images by exploiting their self-similarity, thereby achieving high levels of performance; however, we cannot always use fractal image compression as a standard compression technique because some compressed images are of low quality. Generally, sufficient time is required for encoding and decoding an image before it can be determined whether the compressed image is of low quality or not. Therefore, in our previous study, we proposed a method to estimate the quality of images compressed using fractal image compression. Our previous method estimated the quality using image features of a given image without actually encoding and decoding the image, thereby providing an estimate rather quickly; however, estimation accuracy was not entirely sufficient. Therefore, in this paper, we extend our previously proposed method for improving estimation accuracy. Our improved method adopts a new image feature, namely lacunarity. Results of simulation showed that the proposed method achieves higher levels of accuracy than those of our previous method.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E101-A No.6 pp.900-903
- Publication Date
- 2018/06/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E101.A.900
- Type of Manuscript
- Special Section LETTER (Special Section on Image Media Quality)
- Category
Authors
Megumi TAKEZAWA
Hokkaido University of Science
Hirofumi SANADA
Hokkaido University of Science
Takahiro OGAWA
Hokkaido University
Miki HASEYAMA
Hokkaido University
Keyword
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Megumi TAKEZAWA, Hirofumi SANADA, Takahiro OGAWA, Miki HASEYAMA, "Estimating the Quality of Fractal Compressed Images Using Lacunarity" in IEICE TRANSACTIONS on Fundamentals,
vol. E101-A, no. 6, pp. 900-903, June 2018, doi: 10.1587/transfun.E101.A.900.
Abstract: In this paper, we propose a highly accurate method for estimating the quality of images compressed using fractal image compression. Using an iterated function system, fractal image compression compresses images by exploiting their self-similarity, thereby achieving high levels of performance; however, we cannot always use fractal image compression as a standard compression technique because some compressed images are of low quality. Generally, sufficient time is required for encoding and decoding an image before it can be determined whether the compressed image is of low quality or not. Therefore, in our previous study, we proposed a method to estimate the quality of images compressed using fractal image compression. Our previous method estimated the quality using image features of a given image without actually encoding and decoding the image, thereby providing an estimate rather quickly; however, estimation accuracy was not entirely sufficient. Therefore, in this paper, we extend our previously proposed method for improving estimation accuracy. Our improved method adopts a new image feature, namely lacunarity. Results of simulation showed that the proposed method achieves higher levels of accuracy than those of our previous method.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E101.A.900/_p
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@ARTICLE{e101-a_6_900,
author={Megumi TAKEZAWA, Hirofumi SANADA, Takahiro OGAWA, Miki HASEYAMA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Estimating the Quality of Fractal Compressed Images Using Lacunarity},
year={2018},
volume={E101-A},
number={6},
pages={900-903},
abstract={In this paper, we propose a highly accurate method for estimating the quality of images compressed using fractal image compression. Using an iterated function system, fractal image compression compresses images by exploiting their self-similarity, thereby achieving high levels of performance; however, we cannot always use fractal image compression as a standard compression technique because some compressed images are of low quality. Generally, sufficient time is required for encoding and decoding an image before it can be determined whether the compressed image is of low quality or not. Therefore, in our previous study, we proposed a method to estimate the quality of images compressed using fractal image compression. Our previous method estimated the quality using image features of a given image without actually encoding and decoding the image, thereby providing an estimate rather quickly; however, estimation accuracy was not entirely sufficient. Therefore, in this paper, we extend our previously proposed method for improving estimation accuracy. Our improved method adopts a new image feature, namely lacunarity. Results of simulation showed that the proposed method achieves higher levels of accuracy than those of our previous method.},
keywords={},
doi={10.1587/transfun.E101.A.900},
ISSN={1745-1337},
month={June},}
Copy
TY - JOUR
TI - Estimating the Quality of Fractal Compressed Images Using Lacunarity
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 900
EP - 903
AU - Megumi TAKEZAWA
AU - Hirofumi SANADA
AU - Takahiro OGAWA
AU - Miki HASEYAMA
PY - 2018
DO - 10.1587/transfun.E101.A.900
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E101-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 2018
AB - In this paper, we propose a highly accurate method for estimating the quality of images compressed using fractal image compression. Using an iterated function system, fractal image compression compresses images by exploiting their self-similarity, thereby achieving high levels of performance; however, we cannot always use fractal image compression as a standard compression technique because some compressed images are of low quality. Generally, sufficient time is required for encoding and decoding an image before it can be determined whether the compressed image is of low quality or not. Therefore, in our previous study, we proposed a method to estimate the quality of images compressed using fractal image compression. Our previous method estimated the quality using image features of a given image without actually encoding and decoding the image, thereby providing an estimate rather quickly; however, estimation accuracy was not entirely sufficient. Therefore, in this paper, we extend our previously proposed method for improving estimation accuracy. Our improved method adopts a new image feature, namely lacunarity. Results of simulation showed that the proposed method achieves higher levels of accuracy than those of our previous method.
ER -
English
