Canonical Huffman Coding Based Image Compression using Wavelet
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Canonical Huffman Coding Based Image Compression using Wavelet Rajiv Ranjan1 Accepted: 11 November 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The explosive growth of digital imaging, especially in the fields of medicine, education, and e-commerce, has made data maintenance and transmission over networks a daunting task. Therefore, the development and use of image compression techniques have become vital for overcoming the problems of storage and transmission of digital image data. Two methods that are extensively used for data compression are Discrete Cosine Transformation and Discrete Wavelet Transform (DWT). In our present study, we have shown the benefits of a DWT-based approach by utilizing the canonical Huffman coding as an entropy encoder. DWT decomposes the image into different sub-bands. These sub bands are known as approximate image and detail images. The approximate image is normalized in the range (0, 1) for obtaining the Canonical Huffman coding bit stream. In a similar way, details coefficients are also normalized in the range (0, 1) for obtaining the canonical Huffman coding bit stream of detail images. Hard thresholding is often used to discard insignificant coefficients of detail images. Our proposed method takes less computing time and has a smaller codebook size than that of conventional Huffman coding. Moreover, the results show an improvement over Wavelet Scalar Quantization often used for image compression of fingerprints. We have applied our method to various popular images and obtained promising PSNR, CR, and BPP that highlight the advantages of our approach and the efficiency of our algorithms. Keywords Canonical huffman coding · Discrete wavelet transform · Image compression · Thresholding
1 Introduction 1.1 General Computer images are data intensive and require large amounts of memory for storage. Besides, their transmission places a high strain on network bandwidths. Compression
* Rajiv Ranjan [email protected] 1
BIT Sindri, Dhanbad, Jharkhand 828123, India
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techniques allow us to significantly reduce image sizes with minimal compromise of image quality and fidelity. Our main objective is to transfer image data from a transmitter to a receiver through the process of compression that reorganizes the data file to make it smaller [1]. It is obvious that the higher the compression ratio, the poorer the quality of the resulting data, and so data compression is a trade-off between image size reduction and image fidelity after compression. Figure 1 represents the block diagram of Image Compression. Firstly, we transform the input image using Forward Transform and then we quantize the resulting image. Next we use Entropy Encoding to produce the compressed Image. Image Compression Techniques are of two types: Lossy and Lossless Technique. In the first case, some amount of information of the original image is lost after compression; lossless compression techniques, on the other hand, conserve all the i
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