Bekir Karlik2025-10-06200612100552, 23364335https://www.scopus.com/inward/record.uri?eid=2-s2.0-33749004546&partnerID=40&md5=ee733879826143679e24438fc634b1d2https://gcris.yasar.edu.tr/handle/123456789/10385This paper presents a lossy compression scheme for biomedical images by using a new method. Image data compression using Vector Quantization (VQ) has received a lot of attention because of its simplicity and adaptability. VQ requires the input image to be processed as vectors or blocks of image pixels. The Finite-state vector quantization (FSVQ) is known to give better performance than the memory less vector quantization (VQ). This paper presents a novel combining technique for image compression based on the Hierarchical Finite State Vector Quantization (HFSVQ) and the neural network. The algorithm performs nonlinear restoration of diffraction-limited images concurrently with quantization. The neural network is trained on image pairs consisting of a lossless compression named hierarchical vector quantization. Simulations results are presented that demonstrate improvements in visual quality and peak signal-to-noise ratio of the restored images. © ICS AS CR 2006. © 2008 Elsevier B.V. All rights reserved.EnglishArtificial Neural Networks, Lossy Compression, Medical Image, Vector Quantization, Algorithms, Medical Imaging, Neural Networks, Signal To Noise Ratio, Vector Quantization, Diffraction-limited Images, Finite State Vector Quantization (hfsvq), Lossy Compression, Image CompressionAlgorithms, Medical imaging, Neural networks, Signal to noise ratio, Vector quantization, Diffraction-limited images, Finite State Vector Quantization (HFSVQ), Lossy compression, Image compressionArticle