BPG-Based Automatic Lossy Compression of Noisy Images with the Prediction of an Optimal Operation Existence and Its Parameters
With a resolution improvement, the size of modern remote sensing images increases. This makes it desirable to compress them, mostly by using lossy compression techniques. Often the images to be compressed (or some component images of multichannel remote sensing data) are noisy. The lossy compression...
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| Format: | Article |
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MDPI AG
2022-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/15/7555 |
| _version_ | 1827626089755705344 |
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| author | Bogdan Kovalenko Vladimir Lukin Sergii Kryvenko Victoriya Naumenko Benoit Vozel |
| author_facet | Bogdan Kovalenko Vladimir Lukin Sergii Kryvenko Victoriya Naumenko Benoit Vozel |
| author_sort | Bogdan Kovalenko |
| collection | DOAJ |
| description | With a resolution improvement, the size of modern remote sensing images increases. This makes it desirable to compress them, mostly by using lossy compression techniques. Often the images to be compressed (or some component images of multichannel remote sensing data) are noisy. The lossy compression of such images has several peculiarities dealing with specific noise filtering effects and evaluation of the compression technique’s performance. In particular, an optimal operation point (OOP) may exist where quality of a compressed image is closer to the corresponding noise-free (true) image than the uncompressed (original, noisy) image quality, according to certain criterion (metrics). In such a case, it is reasonable to automatically compress an image under interest in the OOP neighborhood, but without having the true image at disposal in practice, it is impossible to accurately determine if the OOP does exist. Here we show that, by a simple and fast preliminary analysis and pre-training, it is possible to predict the OOPs existence and the metric values in it with appropriate accuracy. The study is carried out for a better portable graphics (BPG) coder for additive white Gaussian noise, focusing mainly on one-component (grayscale) images. The results allow for concluding that prediction is possible for an improvement (reduction) in the quality metrics of PSNR and PSNR-HVS-M. In turn, this allows for decision-making about the existence or absence of an OOP. If an OOP is absent, a more “careful” compression is recommended. Having such rules, it then becomes possible to carry out the compression automatically. Additionally, possible modifications for the cases of signal-dependent noise and the joint compression of three-component images are considered and the possible existence of an OOP for these cases is demonstrated. |
| first_indexed | 2024-03-09T12:48:57Z |
| format | Article |
| id | doaj.art-5c3bacda85de4dc1a78afe851c732626 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T12:48:57Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-5c3bacda85de4dc1a78afe851c7326262023-11-30T22:09:31ZengMDPI AGApplied Sciences2076-34172022-07-011215755510.3390/app12157555BPG-Based Automatic Lossy Compression of Noisy Images with the Prediction of an Optimal Operation Existence and Its ParametersBogdan Kovalenko0Vladimir Lukin1Sergii Kryvenko2Victoriya Naumenko3Benoit Vozel4Department of Information and Communication Technologies, National Aerospace University, 61070 Kharkiv, UkraineDepartment of Information and Communication Technologies, National Aerospace University, 61070 Kharkiv, UkraineDepartment of Information and Communication Technologies, National Aerospace University, 61070 Kharkiv, UkraineDepartment of Information and Communication Technologies, National Aerospace University, 61070 Kharkiv, UkraineIETR, UMR CNRS 6164, University of Rennes, 22305 Lannion, FranceWith a resolution improvement, the size of modern remote sensing images increases. This makes it desirable to compress them, mostly by using lossy compression techniques. Often the images to be compressed (or some component images of multichannel remote sensing data) are noisy. The lossy compression of such images has several peculiarities dealing with specific noise filtering effects and evaluation of the compression technique’s performance. In particular, an optimal operation point (OOP) may exist where quality of a compressed image is closer to the corresponding noise-free (true) image than the uncompressed (original, noisy) image quality, according to certain criterion (metrics). In such a case, it is reasonable to automatically compress an image under interest in the OOP neighborhood, but without having the true image at disposal in practice, it is impossible to accurately determine if the OOP does exist. Here we show that, by a simple and fast preliminary analysis and pre-training, it is possible to predict the OOPs existence and the metric values in it with appropriate accuracy. The study is carried out for a better portable graphics (BPG) coder for additive white Gaussian noise, focusing mainly on one-component (grayscale) images. The results allow for concluding that prediction is possible for an improvement (reduction) in the quality metrics of PSNR and PSNR-HVS-M. In turn, this allows for decision-making about the existence or absence of an OOP. If an OOP is absent, a more “careful” compression is recommended. Having such rules, it then becomes possible to carry out the compression automatically. Additionally, possible modifications for the cases of signal-dependent noise and the joint compression of three-component images are considered and the possible existence of an OOP for these cases is demonstrated.https://www.mdpi.com/2076-3417/12/15/7555image lossy compressionoptimal operation pointquality predictionnoisediscrete cosine transformautomation |
| spellingShingle | Bogdan Kovalenko Vladimir Lukin Sergii Kryvenko Victoriya Naumenko Benoit Vozel BPG-Based Automatic Lossy Compression of Noisy Images with the Prediction of an Optimal Operation Existence and Its Parameters Applied Sciences image lossy compression optimal operation point quality prediction noise discrete cosine transform automation |
| title | BPG-Based Automatic Lossy Compression of Noisy Images with the Prediction of an Optimal Operation Existence and Its Parameters |
| title_full | BPG-Based Automatic Lossy Compression of Noisy Images with the Prediction of an Optimal Operation Existence and Its Parameters |
| title_fullStr | BPG-Based Automatic Lossy Compression of Noisy Images with the Prediction of an Optimal Operation Existence and Its Parameters |
| title_full_unstemmed | BPG-Based Automatic Lossy Compression of Noisy Images with the Prediction of an Optimal Operation Existence and Its Parameters |
| title_short | BPG-Based Automatic Lossy Compression of Noisy Images with the Prediction of an Optimal Operation Existence and Its Parameters |
| title_sort | bpg based automatic lossy compression of noisy images with the prediction of an optimal operation existence and its parameters |
| topic | image lossy compression optimal operation point quality prediction noise discrete cosine transform automation |
| url | https://www.mdpi.com/2076-3417/12/15/7555 |
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