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An Image Compression Approach Based on Convolutional AutoEncoder

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Artificial Intelligence and Green Computing (ICAIGC 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 806))

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Abstract

In recent years, neural networks have demonstrated their robustness and effectiveness in many fields mainly in computer vision tasks. Hence, recently researchers in image processing domain exploit neural networks, powerful ability of representing data, to develop different compression schemes. These schemes yield images with great visual quality and high compression ratio. In this paper, we propose a compression scheme that takes advantages of convolutional Auto-Encoder (CAE) to improve image compression performance. First, an RGB image is converted to the luminance/chrominance space YCbCr then the luminance component Y is compressed using an auto-encoder based convolutional neural network (CNN) whereas the chrominance components CbCr are sub-scaled. Different parameters used to evaluate the efficiency of our proposed method are: Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Multi Scale Structural Similarity Index Measure (MS-SSIM). The results obtained show the effectiveness of our proposed approach.

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Authors and Affiliations

  1. Data Science For Sustainable Earth Laboratory (Data4Earth), Faculty of Sciences and Technics, Sultan Moulay Slimane University, Beni Mellal, BP 325, Morocco

    Oussama Jannani, Najlae Idrissi & Houda Chakib

Authors
  1. Oussama Jannani
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  2. Najlae Idrissi
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  3. Houda Chakib
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Corresponding author

Correspondence to Oussama Jannani .

Editor information

Editors and Affiliations

  1. Data Science for Sustainable Earth Laboratory, Faculty of Sciences and Techniques, Sultan Moulay Slimane University, Beni Mellal, Morocco

    Najlae Idrissi

  2. Data Science for Sustainable Earth Laboratory, Faculty of Sciences and Techniques, Sultan Moulay Slimane University, Beni Mellal, Morocco

    Abdellatif Hair

  3. ENSIAS, Mohammed V University, Rabat, Morocco

    Mohamed Lazaar

  4. Data Science for Sustainable Earth Laboratory, Faculty of Sciences and Techniques, Sultan Moulay Slimane University, Beni Mellal, Morocco

    Youssef Saadi

  5. Data Science for Sustainable Earth Laboratory, Faculty of Sciences and Techniques, Sultan Moulay Slimane University, Beni Mellal, Morocco

    Mohammed Erritali

  6. Faculty of Sciences and Techniques, Hassan First University, Settat, Morocco

    Said El Kafhali

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Jannani, O., Idrissi, N., Chakib, H. (2023). An Image Compression Approach Based on Convolutional AutoEncoder. In: Idrissi, N., Hair, A., Lazaar, M., Saadi, Y., Erritali, M., El Kafhali, S. (eds) Artificial Intelligence and Green Computing. ICAIGC 2023. Lecture Notes in Networks and Systems, vol 806. Springer, Cham. https://doi.org/10.1007/978-3-031-46584-0_7

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