A novel non-customary method of image compression based on image spectrum
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Sådhanå (2020)45:288 https://doi.org/10.1007/s12046-020-01519-7
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A novel non-customary method of image compression based on image spectrum HIMANSHU KUMAR1,* , SUMANA GUPTA2 and K S VENKATESH2 1
Department of Electrical Engineering, IIT Jodhpur, Jodhpur, India Department of Electrical Engineering, IIT Kanpur, Kanpur, India e-mail: [email protected]; [email protected]; [email protected]
2
MS received 5 October 2018; revised 2 September 2020; accepted 13 September 2020 Abstract. Compression of multimedia content is an important processing step and backbone of real life applications in terms of optimum resource utilization in transmission and storage. It is an established field of research with very little scope for further improvement in achieved compression through customary codingbased compression techniques. Consequently, non-customary compression methods have become an important area for future research. Based on the principle ‘Any information that can be restored can be compressed’, we propose a novel spectrum-based image compression technique to further reduce the data footprint with satisfactory quality metric for images. We first blur the image with a point spread function (PSF) determined using frequency content of the given image. Blurring increases the DC component in the image, which in turn gets further compressed compared with original image by DCT-based JPEG compression. To recover the image, we perform deconvolution using the known blur PSF. Results obtained show further improvement of 20 30% in achieved compression with respect to original JPEG compressed image with satisfactory quality of recovered image. Keywords.
Image compression; spectrum; zero preservation; deconvolution; deblurring.
1. Introduction Compression is an important and established field of research in the area of image and video processing. Rapid increases of various devices and technologies pose a serious question on storage and transmission of existing multimedia content between various sources and destinations. Compression enables us to address these challenges by providing efficient storage and transmission of multimedia content. In this area of research, focus has been on coding the content efficiently based on resolution of devices, resources and human perception. Thus the aim of compression is to remove the various redundancies present in data, viz coding redundancy [1–3], pixel-correlation redundancy [4, 5] and perceptual redundancy [6–8]. Compression methods focusing on reducing coding and pixel-correlation redundancy are lossless, while methods that focus on reducing the perceptual redundancy are lossy. State of the art methods use a combination of these techniques to achieve the optimum compression; for example, efficient coding such as Huffman coding [1] is used to reduce coding redundancy after any encoding that reduces the data footprints (pixel-correlation redundancy). *For correspondence
In such aspects, the scope for significant improvement over present
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