Pub Date : 2019-04-29DOI: 10.1002/9781119555414.ch9
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Pub Date : 2011-03-01DOI: 10.1002/9781118032404.CH3
Van Fleet, J. Patrick
One of the main application areas of wavelet transforms is image processing. Wavelet transforms can be used in processes designed to compress images, search for edges in images, or enhance image features. This chapter presents the basics of digital images. It explains how a grayscale (monochrome) image can be interpreted by computer software, how one can represent a grayscale image using a matrix, and how matrices can be used to manipulate images. The chapter also presents an introduction to some basic intensity transformations. It also deals with digital color images as well as some popular color spaces that are useful for image processing. An important step in image compression is that of data encoding. Huffman coding, introduced by David Huffman, is an example of lossless compression. The chapter also explains how to calculate the cumulative energy of a signal or image, and discusses the qualitative measures: entropy and peak signal‐to‐noise ratio.
{"title":"AN INTRODUCTION TO DIGITAL IMAGES","authors":"Van Fleet, J. Patrick","doi":"10.1002/9781118032404.CH3","DOIUrl":"https://doi.org/10.1002/9781118032404.CH3","url":null,"abstract":"One of the main application areas of wavelet transforms is image processing. Wavelet transforms can be used in processes designed to compress images, search for edges in images, or enhance image features. This chapter presents the basics of digital images. It explains how a grayscale (monochrome) image can be interpreted by computer software, how one can represent a grayscale image using a matrix, and how matrices can be used to manipulate images. The chapter also presents an introduction to some basic intensity transformations. It also deals with digital color images as well as some popular color spaces that are useful for image processing. An important step in image compression is that of data encoding. Huffman coding, introduced by David Huffman, is an example of lossless compression. The chapter also explains how to calculate the cumulative energy of a signal or image, and discusses the qualitative measures: entropy and peak signal‐to‐noise ratio.","PeriodicalId":273650,"journal":{"name":"Discrete Wavelet Transformations","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114494368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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