Searchless Fractal Image Compression Using Fast DCT and Real DCT
Growing need for pictorial data in information era makes image storage and transmission very expensive. Fast algorithms to compress visual information without degrading the quality are of utmost importance. To overcome this problem, this paper proposes ne
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Abstract Growing need for pictorial data in information era makes image storage and transmission very expensive. Fast algorithms to compress visual information without degrading the quality are of utmost importance. To overcome this problem, this paper proposes new methods to reduce the encoding time for no search fractal image compression in DCT domain by curtailing the computational complexity of the discrete cosine transform (DCT) equations. Fast DCT and real DCT are the techniques, which are employed for the purpose of increasing the performance of searchless DCT. FDCT uses the concept of fast Fourier transform (FFT) which acts as fast discrete cosine transform (FDCT). Real DCT performs only real calculations and omits the imaginary complexity of the DCT. Proposed methods perform the calculations involved in DCT to be computed faster while keeping the quality of the image as much as nearly possible. Furthermore, the experimental results specified show the effectiveness of the methods being proposed for grayscale images. Keywords Fractal image compression search Adaptive FIC
Fast DCT
FFT
Real DCT
No
1 Introduction Digital image compression is gaining high importance in the era of information and technology. Databases containing thousands of images are becoming an integral part of industry and academia. Fractal image compression is a highly potential Preedhi Garg (&) Richa Gupta CSE Department, Amity University, Noida, Uttar Pradesh, India e-mail: [email protected] Richa Gupta e-mail: [email protected] R.K. Tyagi IT Department, KIET, Ghaziabad, Uttar Pradesh, India e-mail: [email protected] © Springer Science+Business Media Singapore 2016 M. Pant et al. (eds.), Proceedings of Fifth International Conference on Soft Computing for Problem Solving, Advances in Intelligent Systems and Computing 436, DOI 10.1007/978-981-10-0448-3_7
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image compression technique. Fractal image compression can serve the increasing demands of efficient transmission and storage of pictorial data. Self-similarity is the key property of fractals on which the fractal image compression is based [1]. The idea of fractal image compression was first introduced and executed by Barnsley and Sloan [2]. Jacquin [3] proposed a more practical approach for fractal image compression by introducing partitioned iterative system called PIFS. Jacquin achieved good image quality that too at higher compression rates but came across some drawbacks of high computational time for encoding procedures. The high computational demand as well as the existence of best match between domain and range block makes the execution of fractal image compression arduous. Matching of domain and range blocks is an extensively used operation which is very time-consuming and restricts the usage of fractal image compression in various applications. Many researchers focused on decreasing the complexity of encoding algorithm such as Chong Fu et al. introduced the concept of DCT-based fractal image compression to increa
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