Image Compression System using ANN

The rapid growth of digital imaging applications, including desktop publishing, multimedia, teleconferencing, and high definition television (HDTV) has increased the need for effective and standardized image compression techniques. Among the emerging standards are JPEG, for compression of still images; MPEG, for compression of motion video; and CCITT H.261 (also known as Px64), for compression of video telephony and teleconferencing. All three of these standards employ a basic technique known as the discrete cosine transform (DCT), Developed by Ahmed, Natarajan, and Rao [1974]. Image compression using Discrete Cosine Transform (DCT) is one of the simplest commonly used compression methods. The quality of compressed images, however, is marginally reduced at higher compression ratios due to the lossy nature of DCT compression, thus, the need for finding an optimum DCT compression ratio. An ideal image compression system must yield high quality compressed images with good compression ratio, while maintaining minimum time cost. The neural network associates the image intensity with its compression ratios in search for an optimum ratio.

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DESIGN OF NEURAL NETWORK AS DATA FLOW MODEL FOR IMAGE COMPRESSION

International Journal of Image Processing and Vision Science ◽

10.47893/ijipvs.2013.1037 ◽

2013 ◽

pp. 195-199

Author(s):

DANESHWARI I. HATTI ◽

SAVITRI RAJU ◽

MAHENDRA M. DIXIT

Keyword(s):

Neural Network ◽

Image Compression ◽

Discrete Cosine Transform ◽

Compression Ratio ◽

Convergence Time ◽

Training Algorithms ◽

Hardware Complexity ◽

Cosine Transform ◽

Hidden Neurons

In digital communication bandwidth is essential parameter to be considered. Transmission and storage of images requires lot of memory in order to use bandwidth efficiently neural network and Discrete cosine transform together are used in this paper to compress images. Artificial neural network gives fixed compression ratio for any images results in fixed usage of memory and bandwidth. In this paper multi-layer feedforward neural network has been employed to achieve image compression. The proposed technique divides the original image in to several blocks and applies Discrete Cosine Transform (DCT) to these blocks as a pre-process technique. Quality of image is noticed with change in training algorithms, convergence time to attain desired mean square error. Compression ratio and PSNR in dB is calculated by varying hidden neurons. The proposed work is designed using MATLAB 7.10. and synthesized by mapping on Vertex 5 in Xilinx ISE for understanding hardware complexity. Keywords - backpropagation, Discrete

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Image Compression Using Permanent Neural Networks for Predicting Compact Discrete Cosine Transform Coefficients

International Journal of Engineering and Technology Innovation ◽

10.46604/ijeti.2021.6925 ◽

2021 ◽

Vol 11 (2) ◽

pp. 122-134

Author(s):

Saleh Alshehri

Keyword(s):

Neural Networks ◽

Image Compression ◽

Discrete Cosine Transform ◽

Compression Ratio ◽

Discrete Cosine Transformation ◽

Compression Technique ◽

Cosine Transform ◽

Image Measure ◽

Transform Coefficients ◽

Execution Times

This study proposes a new image compression technique that produces a high compression ratio yet consumes low execution times. Since many of the current image compression algorithms consume high execution times, this technique speeds up the execution time of image compression. The technique is based on permanent neural networks to predict the discrete cosine transform partial coefficients. This can eliminate the need to generate the discrete cosine transformation every time an image is compressed. A compression ratio of 94% is achieved while the average decompressed image peak signal to noise ratio and structure similarity image measure are 22.25 and 0.65 respectively. The compression time can be neglected when compared to other reported techniques because the only needed process in the compression stage is to use the generated neural network model to predict the few discrete cosine transform coefficients.

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Robust Image Compression Algorithm using Discrete Fractional Cosine Transform

WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL ◽

10.37394/23203.2022.17.3 ◽

2022 ◽

Vol 17 ◽

pp. 25-33

Author(s):

Vivek Arya

Keyword(s):

Image Compression ◽

Compression Ratio ◽

Fractional Fourier Transform ◽

Signal To Noise Ratio ◽

Quality Parameters ◽

Cosine Transform ◽

Time Frequency ◽

Compressed Images ◽

Discrete Fractional Fourier Transform ◽

Image Compression Algorithm

The discrete fractional Fourier transform become paradigm in signal processing. This transform process the signal in joint time-frequency domain. The attractive and very important feature of DFrCT is an availability of extra degree of one free parameter that is provided by fractional orders and due to which optimization is possible. Less execution time and easy implementation are main advantages of proposed algorithm. The merit of effectiveness of proposed technique over existing technique is superior due to application of discrete fractional cosine transform by which higher compression ratio and PSNR are obtained without any artifacts in compressed images. The novelty of the proposed algorithm is no artifacts in compressed image along with good CR and PSNR. Compression ratio (CR) and peak signal to noise ratio (PSNR) are quality parameters for image compression with optimum fractional order.

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