INTELLIGENT COMPRESSION TECHNIQUES IN ART PHOTOGRAPHY
DOI:
https://doi.org/10.29121/shodhkosh.v6.i2s.2025.6728Keywords:
Intelligent Image Compression, Art Photography, Convolutional Neural Networks (CNNs), Autoencoders, Generative Adversarial Networks (GANs)Abstract [English]
In art photography, visual fidelity is an exceptionally high level of performance, in which artistic intent can easily be changed by minute distortions. Nonetheless, high-resolution art images are difficult to store and transmit, because they are large files, and have complicated visual representations. Conventional compression algorithms (e.g. JPEG, PNG, TIFF, etc.) tend to reduce small details, shading and texture that is critical to maintain the artistic expression. This paper discusses intelligent compression algorithms, which can be used to implement better compression rates without the need to compromise the perceptual quality of the image through machine learning and deep learning models. Some intelligent algorithms, including Convolutional Neural Networks (CNNs) to extract hierarchical features, Autoencoders to learn dimensionality reduction, and Generative Adversarial Networks (GANs) to recover perceptual quality are applied on a dataset of various art photography samples and compared to traditional methods. The evaluation uses quantitative measures Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and file size reduction, in which the qualitative measures of visual integrity are also made. The findings show that intelligent compression has a better fidelity at lower bitrates with artistic textures and tonal variations. More so, the adaptive bitrate distribution, and the perceptual optimization methods make sure that compression is dynamically responsive to the visual complexity. This study highlights the opportunities of the neural compression systems in the process of digital art preservation, archiving, and online curating that precondition the further development of the art photography digitization in which aesthetical quality is as crucial as efficiency.
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Copyright (c) 2025 Pooja Yadav, Sangeet Saroha, Shardul Phansalkar, Sunila Choudhary, Prateek Garg, Dr. Keerti Rai
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