Wavelets For Image Compression Research Articles

Cupolets: History, Theory, and Applications

In chaos control, one usually seeks to stabilize the unstable periodic orbits (UPOs) that densely inhabit the attractors of many chaotic dynamical systems. These orbits collectively play a significant role in determining the dynamics and properties of chaotic systems and are said to form the skeleton of the associated attractors. While UPOs are insightful tools for analysis, they are naturally unstable and, as such, are difficult to find and computationally expensive to stabilize. An alternative to using UPOs is to approximate them using cupolets. Cupolets, a name derived from chaotic, unstable, periodic, orbit-lets, are a relatively new class of waveforms that represent highly accurate approximations to the UPOs of chaotic systems, but which are generated via a particular control scheme that applies tiny perturbations along Poincaré sections. Originally discovered in an application of secure chaotic communications, cupolets have since gone on to play pivotal roles in a number of theoretical and practical applications. These developments include using cupolets as wavelets for image compression, targeting in dynamical systems, a chaotic analog to quantum entanglement, an abstract reducibility classification, a basis for audio and video compression, and, most recently, their detection in a chaotic neuron model. This review will detail the historical development of cupolets, how they are generated, and their successful integration into theoretical and computational science and will also identify some unanswered questions and future directions for this work.

Comparative Analysis of Wavelet Families in Image Compression, Featuring the Proposed New Wavelet

Image compression is fundamental to the efficient and cost-effective use of digital media, including but not limited to medical imagery, satellite images, and daily photography. Wavelet transform is one of the best methods used in compression. This study conducts a meticulous comparative analysis of various established wavelet families and introduces a novel wavelet named new wavelet for image compression (NWI), shedding light on its performance compared to well-established counterparts. This research conducts a meticulous comparative analysis of various wavelet families to assess their performance in image compression. The results show that an average compression ratio of around 75% can be achieved with a 38 dB PSNR value for all test images. The best result was achieved with the test-2 image, with a compression performance (CP) of 3312.08, using the proposed NWI wavelet. The research evaluates eight wavelet families and shows that the performance of image compression depends on both image type and selected wavelet family while keeping the coding algorithm the same for all calculations of image processing scenarios. In image compression, introducing new wavelet families, such as the NWI, can enhance performance and achieve better results.

Compression of image using hybrid combination of haar wavelet transform and color space model in noisy environment

This article manages the examination of image compression utilizing crossover blend of both wavelet change and color space.. The paper covers foundation which incorporates decay of image, thresholding in wavelet for image compression. The compressed image of Haar wavelet is then gone through color space transformation which is utilized to change the color image into grey scale image. On transformation to grey scale image, the number of bits and transfer speed gets diminished. It is conceivable to process the images of various organizations inside the security issues that have been additionally discussed and actualized. The outcomes are shown as depictions in the results.

Selection of wavelet for image compression in hybrid coding scheme combining SPIHT- and SOFM-based vector quantisation

Image compression is an important task for image transmission and storage. Thus far many compression techniques have been developed, such as transform image coding, predictive image coding, Vector Quantisation (VQ) and so on. Of these compression techniques, the techniques based on transform coding and VQ have received considerable attention. Recently, hybrid schemes for effective image compression have gained enormous popularity among researchers. In this paper, we analyse a hybrid scheme combining Kohonen’s Self-Organising Feature Map (SOFM)-based VQ coding and Set Partitioning In Hierarchical Trees (SPIHT) coding for compression of images (Rawat and Meher, 2009). The reconstructed image quality achieved after decoding depends upon the wavelet used in SPIHT. The effectiveness of the scheme for various orthogonal and biorthogonal wavelets for the hybrid scheme is tested in terms of Peak Signal-to-Noise Ratio (PSNR) and Visual Information Fidelity (VIF).

General construction of wavelet filters via a lifting scheme and its application in image coding

By using a lifting scheme, we present a technique to construct compactly supported wavelets whose coefficients are composed of free variables that lie in an interval. By selecting the coefficients of the 9 to 7 wavelet filter and associated lifting scheme, an efficient approach via wavelets for image compression is developed. Furthermore, the rationalized coefficients wavelet filter that can be implemented with simple integer arithmetic is achieved, and its characteristic is close to the well-known original irrational coefficients 9 to 7 wavelet filters developed by Cohen, Daubechies, and Feauveau. Software and hardware simulations show that the new method has very low complexity, and simultaneously preserves a high quality of a compressed image.

Pre-post quantisation and integer wavelet for image compression

Quantisation prior to the integer wavelet transform (Pre-Qnt) is introduced. The best combination of the Pre-Qnt and the conventional post-quantisation (Post-Qnt) in multi-stage band splitting based on the integer wavelet transform is presented.

Popular biorthogonal wavelet filters via a lifting scheme and its application in image compression

A technique is presented for constructing compactly supported wavelets whose coefficients are composed of free variables locating in an interval by using a lifting scheme. An efficient approach-based wavelet for image compression is developed by selecting the coefficients of the 9–7 wavelet filter and associated lifting scheme. Furthermore, the rationalised coefficients wavelet filter that can be implemented with simple integer arithmetic is achieved and its characteristic is close to the well known original irrational coefficients 9–7 wavelet filters developed by Cohen, Daubechies and Feauveau. To further reduce the computational cost of image coding applications, an acceleration technique is proposed for the lifting steps. Software and hardware simulations show that the new method has very low complexity, and simultaneously preserves the high quality of the compressed image.