Hybrid optimal algorithm-based 2D discrete wavelet transform for image compression using fractional KCA
- PDF / 2,874,120 Bytes
- 16 Pages / 595.276 x 790.866 pts Page_size
- 41 Downloads / 197 Views
REGULAR PAPER
Hybrid optimal algorithm‑based 2D discrete wavelet transform for image compression using fractional KCA V. Geetha1 · V. Anbumani1 · G. Murugesan1 · S. Gomathi1 Received: 8 April 2020 / Accepted: 3 August 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Due to the low compression performance of traditional compression models, we have developed a new HOA based Fractional KCA with 2D-DWT for improving the multispectral image quality. In this paper, we present a novel multispectral image compression method for improving the complexity by maintaining quality reconstruction and also reducing the size of the storage of multispectral images. Initially, Karhunen–Loeve transform (KLT) is used to remove the spatial redundancies. In the second stage, 2D DWT is used to eliminate the intraband spatial redundancies. In the third stage, Fractional KCA (FKCA) is applied to improve the post-transformation process. FKCA is connected to the band of all wavelet sub-bands to minimize the spatial redundancy between intra sub-bands. Finally, the Hybrid Optimal algorithm (HOA) based FKCA is used to eliminate the residual and information redundancy among the neighboring bands. The experimental analysis of proposed 2D-DWT based Fractional KCA shows that the model improves the performance of compression data in terms of PSNR, MSSI, and VIF. Also, the multispectral image dataset shows the proposed compression model outperforms the existing compression models such as FKLT + PCA, ADWT + OADL, and DWT + DCT Keywords Discrete wavelet transform · Remote sensing · Spatial redundancy · Multispectral images · Image compression · Fractional KCA · Hybrid optimal algorithm
1 Introduction In recent years, discrete wavelet transforms (DWT) have been applied in various applications of signal processing. This is primarily because of its ability to offer the signal information of the time domain and frequency domain. 2D DWT has been applied in various image applications such as image enhancement, image decomposition, noise removal, image compression, and image fusion. In real-time signal processing, the hardware performs better than software concerning cost, power, area, and operation speed [1]. Similarly, 2D DWT’s VLSI framework design in real-time image processing has been necessary. This VLSI structural design of 2D DWT depends on bit-parallel and bit-serial distributed arithmetic (DA). Here the hardware needs are less, and in Communicated by Y. Zhang. * V. Geetha [email protected] 1
Department of ECE, Kongu Engineering College, Perundurai, Erode, India
bit-serial DA architecture depends on DWT design slowly over time. Moreover, bit-parallel DA is quicker than the bit-serial-based architectural design. Though, a bit-parallel DA based design requires more memory. DA is the efficacy method for the understanding of higher-order filters, and it attains maximum throughput devoid of the support of hardware multiplier. The bit-serial process that creates the sum of products in a specific amount of clock cycles, a
Data Loading...
