Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/4971
Title: A Framework for Hardware Efficient Reusable IP Core for Grayscale Image CODEC
Authors: Sengupta, Anirban
Roy, Dipanjan
Keywords: Adders;Computer hardware;Hardware;Image coding;Intellectual property core;Internet protocols;Medical applications;Medical imaging;NASA;Network function virtualization;Pixels;Wavelet transforms;CODEC;Codecs;IP networks;Pixel intensities;Wavelet coefficients;Image compression
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers Inc.
Citation: Sengupta, A., Roy, D., Mohanty, S. P., & Corcoran, P. (2017). A framework for hardware efficient reusable IP core for grayscale image CODEC. IEEE Access, 6, 871-882. doi:10.1109/ACCESS.2017.2776293
Abstract: This paper proposes two major novelties. First, we provide a mathematical framework for hardware resource efficient IP core-based image compression and decompression (CODEC). The framework includes CODEC functions that are capable of determining the pixel intensities of a compressed gray scale image using significantly lesser hardware resources. Digital pixel values of the original image are fed as an input to the functions of proposed IP framework and compressed digital pixel values of compressed image generated. Similarly, digital pixel values of the compressed image are fed into other functions of the proposed framework for image decompression. Second, the second novelty is using the derived IP functions to propose designs of reusable IP cores for complete Haar wavelet transformation (HWT)-based lossy image CODEC. Testing of images from various data sets (NASA, medical applications, and so on) in terms of hardware resources, image quality, and compression efficiency have indicated that the proposed IP core framework was successful in achieving hardware efficient CODEC compared with JPEG and conventional HWT CODECs. © 2013 IEEE.
URI: https://doi.org/10.1109/ACCESS.2017.2776293
https://dspace.iiti.ac.in/handle/123456789/4971
ISSN: 2169-3536
Type of Material: Journal Article
Appears in Collections:Department of Computer Science and Engineering

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