Empirical Analysis on the Effect of Image Compression and Denoising Using Different Wavelets on Iris Recognition

We have considered, in this paper, two compression methods based on wavelet transform and progressive coding, called EZW (Embedded Zerotree Wavelet) and SPIHT (Set partitioning in hierarchical trees). These techniques provide the opportunity to significantly increase compression ratios while preserving high qualities of reconstructed medical images. Unlike the EZW, the SPIHT approach takes advantage of subband correlations at near and far resolution levels (father-son relationships). A partial ordering by amplitude of the wavelet coefficients of the DWT (Discrete Wavelet Transform), partitioning in hierarchical trees, and scheduling of the transmission of the refinement bits are the three ideas used by SPIHT (the amplitude of each significant coefficient is progressively refined). The goal is to create a digital tool with the required main compression rate and PSNR (Peak Signal to Noise Ratio) limitations for compressing medical images. A development of the two cited algorithms has been carried out. For their evaluation, several medical images from two databases were used. The results prove that these algorithms are very efficient. Also, it has been found that the obtained results with SPIHT method greatly exceed those provided by EZW approach. SPIHT approach is very efficient compared to EZW from the point of view of the compression ratio and the image quality.

The purpose of this research paper is to investigate, explore, and analyze the coding performance of contemporary wavelet methodologies on medical images used for medical diagnostics, including Embedded Zerotree Wavelet (EZW), Set Partition in Hierarchical Trees (SPIHT), Spatial Orientation Tree Wavelet (STW), Wavelet Difference Reduction (WDR), and Adaptively Scanned Wavelet Difference Reduction (ASWDR). Rate-distortion methods of preparation are theoretically bound for compression static resources given an integrity requirement. However, there is no well-known limit for seemingly non-stationary pictures. The lack of a theoretical lower limit complicates the issue of photo compression. It is uncertain how much farther a picture can be compressed. There is no theoretically adequate metric of deformation that can be used to evaluate the performance of distinct images. Choosing the most suited wavelet filter for lossless image compression of clinical imaging data is one of the problems and challenging issues. We evaluated the trade between reconstructed picture quality and coder type in our research, and the simulation findings are reported in this paper. Transform coding is a well-known technique for compressing visual data. Two-dimensional (2-D) photographs are transferred from the spatial domain to the frequency domain via a transform-based compression approach. A successful transformation will compress useful information into a limited number of low-frequency transform coefficients. An HVS is more sensitive to low spatial frequency energy than it is to high spatial frequency energy. Thus, compression may be performed by quantizing the coefficients, such that only the most significant coefficients (low-frequency coefficients) are conveyed and the others are removed. By picking a wavelet function, quantizer, and statistical coder, a wavelet image compression system may be developed. We evaluated the influence of wavelet analysis and depiction, compression ratio, picture content, and resolution on image quality using a few various types of wavelets. We demonstrate in this paper that while looking for the optimum wavelet, it is necessary to take into consideration both objective and subjective metrics of image quality. We emphasize the DWT's performance advantage over the DCT.

We investigate the issue of efficient data organization and representation of the curved wavelet coefficients [curved wavelet transform (WT)]. We present an adaptive zero-tree structure that exploits the cross-subband similarity of the curved wavelet transform. In the embedded zero-tree wavelet (EZW) and the set partitioning in hierarchical trees (SPIHT), the parent-child relationship is defined in such a way that a parent has four children, restricted to a square of 2×2 pixels, the parent-child relationship in the adaptive zero-tree structure varies according to the curves along which the curved WT is performed. Five child patterns were determined based on different combinations of curve orientation. A new image coder was then developed based on this adaptive zero-tree structure and the set-partitioning technique. Experimental results using synthetic and natural images showed the effectiveness of the proposed adaptive zero-tree structure for encoding of the curved wavelet coefficients. The coding gain of the proposed coder can be up to 1.2 dB in terms of peak SNR (PSNR) compared to the SPIHT coder. Subjective evaluation shows that the proposed coder preserves lines and edges better than the SPIHT coder.

This paper describes a family of list-free tree-set scanning (LIFTS) algorithms, related to Shapiro's (1993) embedded zero-tree wavelet (EZW) coding and to Said and Pearlman's (see IEEE Trans. on Circuits and Systems for Video Technology, vol.6, no.3, p.243-9, 1996) set partitioning in hierarchical trees (SPIHT) algorithm. The principal advantage of the LIFTS algorithms is that they are parallelizable and require greatly reduced computational complexity and memory overhead, while nearly achieving the efficiency of SPIHT. The algorithms may be tailored to special requirements, such as transmission of a fully embedded bit-stream with sub-image refinement.

In this paper an adaptive variable degree-k zero-tree (AVDZ) algorithm is proposed for re-encoding of perceptually quantized wavelet packet transform coefficients of high quality wideband speech. Its performance is compared with two well-known schemes comprising: 1- Embedded Zero-tree Wavelet (EZW) and 2- The set partitioning in hierarchical trees (SPIHT). This comparison is carried out using the speech modified versions of these schemes. It is shown that the proposed AVDZ algorithm outperforms these methods by about 6–10% in coding efficiency and respectively 50% and 35% (on average) in total computation times.

The basic objective of this paper is to analyze the concept of wavelet based algorithms for image compression using different parameter. All algorithms are based on still images, The algorithm involved in the comparative analysis is Wavelet Difference Reduction (WDR), Spatial orientation tree wavelet (STW), Embedded zero tree wavelet (EZW) and modified Set Partitioning in hierarchical trees (SPIHT). These algorithms are more effective and deliver a better feature in the image. In compression, wavelets transform have shown a good elasticity to a large amount of data, while being of realistic complexity. These techniques are used in many image processing applications. The techniques are compared by using the performance parameters peak signal to noise ratio (PSNR) & mean square error (MSE).

In recent years there has been widely increase the use of digital media everywhere. To increase the use of digital media, there is a huge problem of storage, manipulation and transmission of data over the internet. These digital media such as image, audio and video require large memory space. So it is necessary to compress the digital data to require less memory space and less bandwidth to transmission of data over network. Image compressions techniques are used to compress the data for reduce the storage requirement. It plays an important role for transfer of data such as image over the network. Two methods are used in this paper on Barbara image. This compression study is performed by using Set Partitioning In Hierarchical Trees (SPIHT) and Embedded Zero tree Wavelet (EZW) compression techniques. There are many parameters are used to compare this techniques. Mean Square Error (MSE), Pick Signal to Noise Ration (PSNR) and Compression Ratio (CR) are used at different level of decompositions.

Lossless compression schemes with progressive transmission capability play a key role in critical data storage, retrieval and transmission, when a large volume of data must be handled fast, without distortions, over large networks with limited bandwidth. Wavelet transforms present one such approach for the purpose of compression. Wavelet based compression methods, when combined with EZW (Embedded Zerotree Wavelet) and SPIHT (Set Partitioning in Hierarchical Trees) algorithms gives high compression ratio along with appreciable image quality which is very much necessary for huge database storage in Medical Centres and medical data transfer for the purpose of diagnosis. The Progressive quality of the compression methods facilitates the user to the best compressed image which does not have recognizable quality loss.In this study wide variety of CT and MRI (512x512) images are used to validate the usefulness of progressive property especially for telemedicine purpose where either single image or sequence of images are transmitted over computer networks at large distances so that they could be used in a multitude of applications.Our main goal is to compare different types of wavelets (Haar, db, Bior 4.4) for medical image compression because of the fact that there is no single wavelet, which will always provide the best performance.

The recent years have witnessed rapid strides in the use of cloud computing and its countless applications. A cloud can contain massive volumes of multimedia data in the form of images, video and audio. Cloud computing platforms confront challenges in terms of data confidentiality, message integrity, user authentication and compression. Multimedia data needs plenty of storage capacity. Consequently, there is a need for multimedia data compression to reduce data size. Compression techniques are quite reliable, offering benefits to organizations dealing with metasized data in the cloud. Compressing large quanta of data leads to superior utilization of cloud storage. Compression techniques can compress data used for storage and transmission, yet compression alone is inadequate because multimedia data shared should, of necessity, be secure. Therefore, both multimedia compression and security are mandatory in the cloud. The chief goal of this paper is to propose a new framework, comprising multiscale transforms, public key cryptography and appropriate encoding techniques, that performs joint medical image compression and image encryption in the cloud. Multiscale transforms play a lead role in image compression, and the ones discussed in this paper include wavelet, bandelet, curvelet, ridgelet and contourlet transforms. Wavelet transforms offer robust localization both in terms of time and frequency domains. Bandelet transforms offer natural images geometric regularity to help improve the efficiency of representation. Curvelet transforms handle curve discontinuities well, with ridgelet transforms being the core idea behind curvelets. Contourlet transforms capture smooth contours and edges at any orientation. The Rivest-Shamir-Adleman (RSA) algorithm is used to encrypt images to provide maximum security when they are being transferred. Encoding techniques involved in this paper comprise the Embedded Zerotree Wavelet (EZW), Set Partitioning in Hierarchical Trees (SPIHT), Wavelet Difference Reduction (WDR), and Adaptively Scanned Wavelet Difference Reduction (ASWDR). Performance parameters such as peak signal to noise ratio (PSNR), mean square error (MSE), image quality index and structural similarity index (SSIM) are used for evaluation. It is justified that the proposed framework compresses images securely in the cloud.

Problem statement: In any real time biometric system processing speed and recognition time are crucial parameters. Reducing processing time involves many parameters like normalization, FAR, FRR, management of eyelid and eyelash occlusions, size of signature etc. Normalization consumes substantial amount of time of the system. This study contributes for improved iris recognition system with reduced processing time, False Acceptance Rate (FAR) and False Rejection Rate (FRR). Approach: To improve system performance and reliability of a biometric system. It avoided the iris normalization process used traditionally in iris recognition systems. The technique proposed here used different masks to filter out iris image from an eye. Comparative study of different masks was done and optimized mask is proposed. The experiment was carried on CASIA database consisting of 756 iris images of 108 persons. Each person contributes seven images of eye (108×7 = 756) images in the database. Results: In the proposed method: (1) Normalization step is avoided; (2) Computational time is reduced by 0.3342 sec; (3) Iris signature size is reduced; (4) Improved performance parameters. (With reduced feature size, proposed method achieves 99.4866% accuracy, 0.0069% FAR, 1.0198% FRR and significant increase in speed of the system). Conclusion: Iris signature proposed was comparatively small just of 1×24 size. Though Daugman’s method gives best accuracy of 99.90% but the iris signature length used by that algorithm is comparatively very high that is 1×2048 phase vector. Also Daugman has used phase information in signature formation. Our method gives a accuracy of 99.474% with a signature of comparatively very small length. This has definitely contributed to improve the speed.

The present study investigates compression of textual images with high compression ratios while preserving or improving the general quality, readability, and optical character recognition of the compressed textual images. A novel textual image compression/decompression approach is proposed in which the compression path includes dynamic range reduction, wavelet transform, and set partitioning in hierarchical trees (SPIHT) encoding. The decompression path employs SPIHT decoding, then inverse wavelet transform, and then the proposed image enhancement technique. The compression and recognition performances of the proposed approach are evaluated using quantitative and qualitative measures that are then compared to those of the JPEG2000, DjVu, and multi-dimensional multi-scale parser approaches. In addition to the conventional rate-distortion curve, mean opinion score (MOS) is used and the novel measures of "breakdown point" and "downfall slope" are defined. The quantitative and qualitative results of the proposed approach have achieved results similar to those of the peak signal-to-noise ratio, but considerably outperformed the other two approaches for average MOS, average recognition rate, and the newly defined measures.

In this paper, a novel scheme for the accelerated analysis of quad-trees in the discrete wavelet spectrum of a digital image is proposed. During the pre-scanning step, the proposed scheme generates objective and specially structured binary codes for the whole set of quad-tree roots (wavelet coefficients) and thereby accumulates facts on the significance of respective descendants (wavelet coefficients comprising quad-trees on the view). The developed scheme can be successfully applied to any zero-tree based image coding procedure, such as the embedded zero-tree wavelet (EZW) algorithm of Shapiro and set partitioning in hierarchical trees (SPIHT) by Said and Pearlman. Exceptionally high performance of the proposed quad-tree analysis scheme, in the sense of image encoding times, is demonstrated using the EZW algorithm and the discrete Le Gall wavelet transform.

Two compression frameworks that are based on a set partitioning in hierarchical trees (SPIHT) and JPEG2000 methods are proposed. The 3D mesh is first transformed to 2D images on a regular grid structure. Then, this image-like representation is wavelet transformed employing an adaptive predictor that takes advantage of the connectivity information of mesh vertices. Then SPIHT or JPEG2000 is applied on the wavelet domain data. The SPIHT based method is progressive because the resolution of the reconstructed mesh can be changed by varying the length of the one-dimensional data stream created by SPIHT algorithm. The results of the SPIHT based algorithm is observed to be superior to JPEG2000 based mesh coder and MPEG-3DGC in rate-distortion.

Objective: Iris recognition is one of the emerging areas as the demand for security in social and personal areas is increasing day by day. The most challenging step in the process of iris recognition is accurate iris localization. Methods/Statistical Analysis: In this paper, we propose an iris recognition method in light of Teaching Learning Based Optimisation (ITLBO) to choose the ideal components subset. The iris information, for the most part, contains a tremendous number of textural elements and a similarly modest number of tests per subject, which make the accurate iris pattern classification challenging. Findings: Feature selection scheme is used to identify the most important and irrelevant features from extracted features set of a relatively high dimension based on some selection criterions. It is not generally handy to gather an extensive number of tests because of some security issues. In this paper, we propose ITLBO to enhance the feature subset determination by consolidating important results from different component choice strategies. Application/Improvements: The principle target of ITLBO is to accomplish an adjust the Recognition Rate (RR), the False Acceptance Rate (FAR), the False Reject Rate (FRR) and they chose feature subset measure. The proposed method is computationally successful with the RR of 97.97 % on the CASIA iris datasets.

This paper describes applying the Set Partitioning in Hierarchical Trees (SPIHT) algorithm using a devised model to compress audio signals. It allows choosing the amount of compression based upon bit rate requirements. A threshold setting model, based on energy and frequency patterns of the signal is used to assist the SPIHT encoder set efficient threshold values, based upon the nature of the audio. It is thus adaptable to the nature of the audio as well as the bit rate requirement. The implementation can be used for storage as well as progressive transmission.