Improved fuzzy transform based image compression and fuzzy median filter based its artifact reduction: pairFuzzy
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ORIGINAL ARTICLE
Improved fuzzy transform based image compression and fuzzy median filter based its artifact reduction: pairFuzzy Deepak Gambhir1 • Navin Rajpal1
Received: 28 November 2014 / Accepted: 2 May 2015 Ó Springer-Verlag Berlin Heidelberg 2015
Abstract During image compression, visually significant edges should be well preserved for human perception. Despite existence of many image compression standards, joint photographic experts group (JPEG) is the most popularly used standard for image compression. However at low bit rate, JPEG compressed images exhibit blocking artifacts that adversely affect the visual image quality. Thus, to produce a high visual quality image at low bit rate, pairFuzzy algorithm that is simple and more efficient as compared to JPEG alongwith the capability of reducing artifact is proposed. The proposed algorithm is carried out in three steps. First, an image is preprocessed using competitive fuzzy edge detection which efficiently detects the edge pixels contained in the image. Second, based on the edge information the image is compressed and decompressed using improved fuzzy transform. Third, the reconstructed image is postprocessed using fuzzy switched median filter for artifact reduction. The subjective as well as objective analysis alongwith the comparison to recent methods proves the superiority of proposed algorithm. Keywords Image compression/decompression Edge detection Fuzzy transform Median filter Artifact reduction
& Deepak Gambhir [email protected] Navin Rajpal [email protected] 1
School of Information and Communication Technology, Guru Gobind Singh Inderprastha University, Dwarka, New Delhi, India
1 Introduction Image compression reduces the amount of data required to represent a digital image. A large size of digital image requires large storage space, large bandwidth and more time for uploading and downloading on the internet. The problem can be solved by reducing the redundant and/or irrelevant information from the image. Image compression is widely used in medical imaging, remote sensing, video conferencing, high definition television, fax etc. Many image compression methods have been developed in literature. Among these commonly used compression techniques include discrete cosine transform (DCT) based such as joint photographic experts group (JPEG), wavelet based methods such as JPEG2000, fuzzy based, neural networks based, optimization techniques based image compression methods [3, 13, 21, 30, 38, 39]. Motivation JPEG based on DCT is the most popularly used image compression standard. However when low bit rate is to be achieved, JPEG produces compressed images that suffer from annoying blocking artifacts. To reduce the blocking artifacts onto the compressed images fuzzy transform is utilized by various researchers [25, 26]. Since fuzzy transform converts a complex problem into a respective problem of linear algebra that deals with vectors making computations easier and also possess an important property of preserving monotonicity [28] that
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