An Efficient Compression of Gray Scale Images Using Wavelet Transform

Abstract

The rapid development of technology and the standardization of digital photography have led to an explosive growth in digital image distribution and reproduction. The enhancement of storage capacity in computer disks and advancement in networking have not been able to keep pace with the demands of handling, storing and finally transmitting huge volume of image data. Only proper image compression technologies seem to offer a solution to this challenge. The importance of digital image compression in multimedia applications has inspired extensive research all over the world. The present study recommends a newly formulated algorithm by computing Discrete Wavelet Transform (DWT) in combination with thresholding and quadtree decomposition. Findings prove that the proposed technique is at par with EZW image compression algorithm in terms of quality performance at the same bit rate, and obviates the need for employing any other conventional standard image compression techniques.

Modelling of Block Feature-Content Based Efficient Image Compression System Using DWT and Machine Learning

“…This suggests a newly developed approach by computing the discrete wavelet transform together with thresholding and quadtree decomposition [12]. This method was used to divide the image into two sections.…”

“…Reconstruction involves querying each block feature with a pretrained K-nearest neighbour (K-NN) model [15]. The proposed work is superior in terms of high compression ratio [2,16], quality reconstruction, and low reconstruction computational time in comparison to prior works [1,12,17].…”

An Improved Image Compression Algorithm Using 2D DWT and PCA with Canonical Huffman Encoding

“…The most widely used transformation methods include the Karhunen-Loeve transform (KLT) [30], Walsh-Hadamard transforms (WHTs) [31], SVD [11], PCA [12], DCT [13], DWT [14] and integer wavelet transform (IWT) [32].…”

“…Hence, DWT was the preferred method for image compression during the present study [14]. Because of its high energy compaction property and lossy nature, this technique can remove unnecessary data from an image to achieve the desired compression level for images.…”

“…In the process of the study, the authors examined several frequently cited images in the available literature. Slice resolu- Secondly, there are image transformation methods, which include singular value decomposition (SVD) [11], principal component analysis (PCA) [12], discrete cosine transform (DCT) [13] and discrete wavelet transform (DWT) [14]. Through these methods, image samples are transformed from the spatial domain to the frequency domain, thereby concentrating the energy of the image in a small number of coefficients.…”