Wavelet Transform Algorithms

Wavelet Transform Algorithms

Arvind Kumar Kourav, Shilpi Sharma, Vimal Tiwari
DOI: 10.4018/978-1-5225-3870-7.ch011
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Digital image processing has an enormous impact on technical and industrial applications. Uncompressed images need large storage capacity and communication bandwidth. Digital images have become a significant source of information in the current world of communication systems. This chapter explores the phenomenon of digital images and basic techniques of digital image processing in detail. With the creation of multimedia, the requirements for the storage of a larger amount of high quality pictures and data analysis are increasing.
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Digital image processing is vital field of engineering and technology, in current era every field are based on the applications of digital image processing, in digital image processing, digital depictions of images commonly require a large number of bits. In various applications, it is significant to study procedures for signifying an image, or the information contained in the image, with fewer bits. By eliminating redundant or unnecessary information, image compression is the activity that addresses this aim. Image processing techniques have been applied in several areas of image and video processing such as communication, video conferencing etc. In the digital image and video compression it is required to reduce bit rate requirement and improves speed of transmission. Image compression techniques are mainly in two groups is lossless and lossy (C. Villegas Q. & J. Climent, 2008). In image de-noising it is required to recover the original image at the output, In both analysis main objective is to improve quality of image in term of PSNR by block transform methods, and compare result for better PSNR.

(1) where ‘a’ is the dilation factor, ‘b’ is the translation factor and ψ(t) is the mother wavelet. 1/√a is an energy normalization term that makes wavelets of different scale has the same amount of energy. In this wavelet based algorithms are used for the comparative analysis of image compression, representation of wavelet base algorithms are shown in Figure 1

Figure 1.

Wavelet Transform Algorithms


Background And Main Focus

In this a series of image compression methods of block transform analysis is discuss. Wavelet transforms algorithms also produce the better result in the field of signal processing and image processing in the last decade, these field analyzed different types of application such as s time-frequency analysis, data and image compression, image segmentation, feature analysis, pattern recognition, image de-noising, echo cancellation etc. All these developments of wavelet transform developed during the past decade (Deepti G. & Shital M., 2003). The theory of wavelets transform is based on the quantum and function analysis in unifying and wavelet analysis is executed by a prototype function called a wavelet function. Wavelets analysis is functions defined in a finite interval and set the having average tends to zero.

Key Terms in this Chapter

Peak Signal to Noise Ratio (PSNR): It is the ratio between maximum powers of signal to noise.

Matrix Laboratory (MATLAB): It is a tool used for matrix and mathematical analysis in the fields of engineering and technology.

Digital Image Processing (DIP): It is the processing techniques of digital images.

Bits Per Pixel (BPP): It denotes number of bits per pixel.

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