Digital image compression technology is of vital importance for the very fast transmission and applicable processing of digital image information on the internet. The main basic aim of image compression is greatly to reduce the number of the image pixel elements with the main intension not to affect the original quality of the original image that is to be compressed. It is usually done by removing the redundancy present in the image. Objective of this paper is to compare few promising compression techniques such as DWT coding, Back Propagation Neural Network (BPNN), new hybrid techniques for compression. DWT improves the quality of compressed image. Back-propagation algorithm can be extensively used as a learning algorithm in Artificial Neural Networks. BPNN comes under Feed-Forward Neural Network Architecture. This type of architecture can be used in approximation of all the problems, which is having high precision. Error correction learning rule is particularly used by this Neural Network. This is very efficient algorithm for Image Compression, which works with the architecture of Artificial Neural Network (ANN). There are different types of parameters, which includes Compression ratio (CR), Peak signal to noise ratio (PSNR), Bits per pixel (BPP) and Mean Square error (MSE). The quality of any compressed image can be assessed using a set of parameters. Then the performance Analysis of different images is carried out (on application of three different algorithms) in this paper. The resulst clearly explais that hybrid Image Compression using hybrid DWT-BP(Discrete Wavelet Transform-Back Propagation) provides better CR and PSNR.

A hybrid discrete wavelet transform with neural network back propagation approach for efficient medical image compression

Color Image Compression Using Vector Quantization and Hybrid Wavelet Transform

This paper presents stable, radix-2, completely recursive discrete cosine transform algorithms DCT-I and DCT-III solely based on DCT-I, DCT-II, DCT-III, and DCT-IV having sparse and orthogonal factors. Error bounds for computing the completely recursive DCT-I, DCT-II, DCT-III, and DCT-IV algorithms having sparse and orthogonal factors are addressed. Signal flow graphs are demonstrated based on the completely recursive DCT-I, DCT-II, DCT-III, and DCT-IV algorithms having orthogonal factors. Finally image compression results are presented based on the recursive 2D DCT-II and DCT-IV algorithms for image size 512 by 512 pixels with transfer block sizes 8 by 8, 16 by 16, and 32 by 32 with 93.75% absence of coefficients in each transfer block.

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Signal Processing based on Stable radix-2 DCT I-IV Algorithms having Orthogonal Factors

Signal flow graph approach to efficient and forward stable DST algorithms

In this paper we are discussing the Fourier transform technique for image transformation and compression. Here, we are presenting the implementation technique for image compression by using the Fourier transform method and analyzing the performance. We are also observing the results after transformation of image with quantization process. The quality of the reconstructed compressed image is also analyzed after applying the de-quantization method. The analysis of image quality is performed by using mean square of error and peak signal noise ratio. The results are showing that it is possible to achieve maximum PSNR value.

https://inpressco.com/wp-content/uploads/2015/04/Paper1061178-1182.pdf

Image Transformation and Compression using Fourier Transformation

In this paper image compression using hybrid wavelet transform is proposed. Hybrid wavelet transform matrix is formed using two component orthogonal transforms. One is base transform which contributes to global features of an image and another transform contributes to local features. Here base transform is varied to observe its effect on image quality at different compression ratios. Different transforms like Discrete Kekre Transform (DKT), Walsh, Real-DFT, Sine, Hartley and Slant transform are chosen as base transforms. They are combined with Discrete Cosine Transform (DCT) that contributes to local features of an image. Sizes of component orthogonal transforms are varied as 16-16, 32-8 and 64-4 to generate hybrid wavelet transform of size 256x256. Results of different combinations are compared and it has been observed that, DKT as a base transform combined with DCT gives better results for size 16x16 of both component transforms.

/pdf/performance-comparison-of-hybrid-wavelet-transform-formed-by-4jk08u23kf.pdf

TanujaSarode

Papers

Performance Comparison of Hybrid Wavelet Transform Formed by Combination of Different Base Transforms with DCT on Image Compression