Abstract
Picture Imperfect! Impact of Noise on the Efficiency of Digital Image Compression
Image compression has become ubiquitous in today’s technology-rich lifestyle as more and more documents and images are being created, stored, and transmitted digitally. The latter two actions are directly impacted by the size of the compressed images. Hence, studying the factors that influence the image size will help improve the compression process efficiency, resulting in substantial savings of money and time, not to mention the ecological benefits.
Redundancy of image data is the foundation principle behind image compression. In other words, the actual ‘content’ of the image, referred to as ‘signal’, is relatively sparse and/or undergoes less frequent/slower transitions. In contrast, imperfections on the image, such as specks, dust, scratches, etc., referred to as ‘noise’, are generally distributed across the whole image, and are sharper than the content data, i.e. they undergo faster transitions. Hence, noise defeats the principle behind image compression method and contributes to inefficiency in terms of size.
This project studies the impact of noise on compressed image size by superimposing synthetic noise on a set of control images and subjecting them through image compression process. The characteristics of the noise viz., density and dimensions, are manipulated and the compressed image sizes are analyzed for trends and quantitative relationships.
Even at noise levels not discernable to the naked eye, the loss of compression efficiency will be potentially high. In turn, these findings may highlight the importance and benefits of developing image clean-up technology.
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