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2013 | OriginalPaper | Chapter

24. Neuro-Curvelet Model for Efficient Image Compression Using Vector Quantization

Authors : Arun Vikas Singh, K. Srikanta Murthy

Published in: Proceedings of International Conference on VLSI, Communication, Advanced Devices, Signals & Systems and Networking (VCASAN-2013)

Publisher: Springer India

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Abstract

In many multimedia applications, such as image storage and transmission image, compression plays a major role. The fundamental objective of image compression is to represent an image with least number of bits of an acceptable image quality. A technique based on second-generation curvelet transform and Back-Propagation Neural Network (BPNN) has been proposed. The image compression is accomplished by approximating curvelet coefficients using BPNN. By applying BPNN into compressing curvelet coefficients, we have proposed a new compression algorithm derived from characteristic of curvelet transform. Initially, the image is translated by fast discrete curvelet transform and then based on their statistical properties; different coding and quantization schemes are employed. Differential Pulse Code Modulation (DPCM) is employed to compress low-frequency band coefficients and BPNN is used to compress high-frequency band coefficients. Subsequently, vector quantization is performed on BPNN hidden layer coefficients, thereby resulting in a reconstructed image with less degradation at higher compression ratios. For a given bits per pixel (bpp), the Curvelet Transform with Back-Propagation Neural Network (BPNN) gives better performance in terms of Peak Signal-to-Noise Ratio (PSNR) and Computation Time (CT) when compared to Wavelet Transform with BPNN and JPEG.

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Metadata
Title
Neuro-Curvelet Model for Efficient Image Compression Using Vector Quantization
Authors
Arun Vikas Singh
K. Srikanta Murthy
Copyright Year
2013
Publisher
Springer India
DOI
https://doi.org/10.1007/978-81-322-1524-0_24