Adaptive Deep Learning Image Compression with Test Time Optimization and Importance Guided Quantization
DOI:
https://doi.org/10.15662/IJEETR.2026.0802468Keywords:
Deep Learning, Image Compression, Test-Time Optimization, Importance Maps, PSNR, SSIM, Adaptive Compression, Residual NetworksAbstract
As the problem of compression of digital images continues to grow exponentially with an ever-increasing range of healthcare, social media, telecommunications, and remote sensing applications, the proposal of ADLIC-TTO (Adaptive Deep Learning Image Compression with Test-Time Optimization) arises, which, at test-time, employs per-image fine-tuning to achieve high-quality reconstruction with the same network structure as comparable networks, but at a competitive compression ratio. The model works on 256 x 256 images that are in RGB color, and it reduces the size of the images to a 256-channel latent image with spatial down sampling that is 8x, and this allows the images to be stored effectively in a reduced size. duction and structural fidelity Instagram In-test fine-tuning on Kodak PhotoCD benchmark dataset with 24 high quality natural images having 200 epochs per image, where the evaluation pipeline calculates automatic PSNR, SSIM, bits-per-pixel (BPP), and compression ratio (CR). 42.18 dB and SSIM of 0.9812, much better than JPEG baseline (PSNR 33.94 dB, SSIM 0.9456) yet with an average compression ratio of 3.24x at BPP 7.41, and has SSIM 0.97 at all test images, which proves its suitability in quality critical applications. Medical imaging, digital archiving, and Professional photography is only a few of the applications
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