A multi-scale convolutional neural network and discrete wavelet transform based retinal image compression Article Swipe
Dalila Chikhaoui
,
Mohammed Beladgham
,
Mohamed Benaissa
,
Abdelmalik Taleb‐Ahmed
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.11591/ijeecs.v38.i1.pp243-253
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.11591/ijeecs.v38.i1.pp243-253
The different applications of medical images have contributed significantly to the growing amount of image data. As a result, compression techniques become essential to allow real-time transmission and storage within limited network bandwidth and storage space. Deep learning, particularly convolutional neural networks (CNN) have marked rapid advances in many computer vision tasks and have progressively drawn attention for being used in image compression. Therefore, we present a method for compressing retinal images based on deep CNN and discrete wavelet transform (DWT). To further enhance CNN capabilities, multi-scale convolutions are introduced into the network architecture. In this proposed method, multiscale CNNs are used to extract useful features to provide a compact representation at the encoding stage and guarantee a better reconstruction quality of the image at the decoding stage. Based on compression efficiency and reconstructed image quality, a wide range of experiments have been conducted to validate the proposed technique performance compared with popular image compression standards and existing deep learning-based methods. At a compression ratio (CR) of 80, the proposed method achieved an average peak signal-to-noise ratio (PSNR) value of 38.98 dB and 96.8% similarity in terms of multi-scale structural similarity (MS-SSIM), demonstrating its effectiveness.
Related Topics
Concepts
Convolutional neural network
Computer science
Artificial intelligence
Wavelet
Wavelet transform
Image compression
Scale (ratio)
Pattern recognition (psychology)
Discrete wavelet transform
Compression (physics)
Image (mathematics)
Computer vision
Image processing
Geography
Materials science
Cartography
Composite material
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.11591/ijeecs.v38.i1.pp243-253
- OA Status
- diamond
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406837762
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4406837762Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.11591/ijeecs.v38.i1.pp243-253Digital Object Identifier
- Title
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A multi-scale convolutional neural network and discrete wavelet transform based retinal image compressionWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-26Full publication date if available
- Authors
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Dalila Chikhaoui, Mohammed Beladgham, Mohamed Benaissa, Abdelmalik Taleb‐AhmedList of authors in order
- Landing page
-
https://doi.org/10.11591/ijeecs.v38.i1.pp243-253Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.11591/ijeecs.v38.i1.pp243-253Direct OA link when available
- Concepts
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Convolutional neural network, Computer science, Artificial intelligence, Wavelet, Wavelet transform, Image compression, Scale (ratio), Pattern recognition (psychology), Discrete wavelet transform, Compression (physics), Image (mathematics), Computer vision, Image processing, Geography, Materials science, Cartography, Composite materialTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.networks | 41 |
| abstract_inverted_index.proposed | 96, 147, 169 |
| abstract_inverted_index.quality, | 135 |
| abstract_inverted_index.validate | 145 |
| abstract_inverted_index.attention | 56 |
| abstract_inverted_index.bandwidth | 32 |
| abstract_inverted_index.conducted | 143 |
| abstract_inverted_index.different | 1 |
| abstract_inverted_index.essential | 22 |
| abstract_inverted_index.guarantee | 116 |
| abstract_inverted_index.learning, | 37 |
| abstract_inverted_index.real-time | 25 |
| abstract_inverted_index.standards | 155 |
| abstract_inverted_index.technique | 148 |
| abstract_inverted_index.transform | 79 |
| abstract_inverted_index.(MS-SSIM), | 191 |
| abstract_inverted_index.Therefore, | 63 |
| abstract_inverted_index.efficiency | 131 |
| abstract_inverted_index.introduced | 89 |
| abstract_inverted_index.multiscale | 98 |
| abstract_inverted_index.similarity | 184, 190 |
| abstract_inverted_index.structural | 189 |
| abstract_inverted_index.techniques | 20 |
| abstract_inverted_index.compressing | 69 |
| abstract_inverted_index.compression | 19, 130, 154, 163 |
| abstract_inverted_index.contributed | 7 |
| abstract_inverted_index.experiments | 140 |
| abstract_inverted_index.multi-scale | 86, 188 |
| abstract_inverted_index.performance | 149 |
| abstract_inverted_index.applications | 2 |
| abstract_inverted_index.compression. | 62 |
| abstract_inverted_index.convolutions | 87 |
| abstract_inverted_index.particularly | 38 |
| abstract_inverted_index.transmission | 26 |
| abstract_inverted_index.architecture. | 93 |
| abstract_inverted_index.capabilities, | 85 |
| abstract_inverted_index.convolutional | 39 |
| abstract_inverted_index.demonstrating | 192 |
| abstract_inverted_index.progressively | 54 |
| abstract_inverted_index.reconstructed | 133 |
| abstract_inverted_index.significantly | 8 |
| abstract_inverted_index.effectiveness. | 194 |
| abstract_inverted_index.learning-based | 159 |
| abstract_inverted_index.reconstruction | 119 |
| abstract_inverted_index.representation | 110 |
| abstract_inverted_index.signal-to-noise | 175 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.02930848 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |