Joint medical image compression–encryption in the cloud using multiscale transform-based image compression encoding techniques

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.

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).

Transform coding has been the focus of the research for image compression. In previous research, the Amplitude and Group Partitioning (AGP) coding scheme is proved to be a low complexity algorithm with higher performance, clearly one of the state-of-art transform coding techniques. However, the previous AGP is used along with the Discrete Cosine Transform (DCT) and the discrete wavelet transform. In this paper, a different transform, the Lapped Orthogonal Transform (LOT), replaces the DCT in conjunction with the AGP. This is the first time LOT and AGP have been combined in a coding method. The definition and design of the LOT are discussed. An objective metric to measure the performance of transform, coding gain, is calculated for both the DCT and the LOT. The LOT has slightly higher coding gain than the DCT. The principles of the LOT based AGP image codec (LOT-AGP) are presented and a complete codec, encoder and decoder, is implemented in software. The performance of the LOT-AGP is compared with other block transform coding schemes: the baseline JPEG codec and the DCT based AGP image codec (DCT- AGP) by both objective evaluation and subjective evaluation. The Peak Signal to Noise Ratio (PSNR) is calculated for these three coding schemes. The two AGP codecs are much better than the JPEG codec on PSNR, from about 1.7 dB to 3 dB depending on bit rate. The two AGP schemes have PSNR differences only to a small degree. Visually, the LOT-AGP has the best-reconstructed images among these three at all bit rates. In addition, the coding results of two other state-of-art progressive image codecs are cited for further comparison. One is the Set Partitioning in Hierarchical Trees (SPIHT) algorithm with a dyadic wavelet transform, and the other is Tran and Nguyen's method with the generalized LOT transform. The AGP coding and the adaptive Huffman entropy coding of LOT-AGP are less complex and the memory usage is smaller than in these two progressive codecs. Comparing these three codecs, i.e. the LOT-AGP and the two progressive codecs in PSNR small only small differences in PSNR. SPIHT has about 1 dB higher PSNR than the LOT-AGP and Tran and Nguyen's method for the test image Lena. For the test image Barbara, the PSNR of the LOT- AGP is about 0.5 dB higher than that of the SPIHT and 0.5 dB lower than that of Tran and Nguyen's method. This low- complexity and high performance codec may provide a new direction for the implementation of image compression.

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.

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.

The recent innovation in digital medical imaging techniques requires the development of high-performance storage and image transmission systems. The biggest problem in digital technology is voluminous amount of data generated. For example, a digital mammographic image of size 1024 × 1024 pixels with 8 bits per pixel requires 8.3 Mb for storing it in original form. Researchers in several studies have demonstrated the need for high rate compression algorithms for medical imaging applications. But the recently reported image compression results indicate that Peak Signal-to-Noise Ratio (PSNR) is outperformed by scalar wavelets. However, it often fails to capture high-frequency information accurately. Interestingly, multiwavelet preserves high-frequency information in mammographic image and provides good energy compaction. The challenge still remains as to how one can better represent the signal for achieving the best compression. This paper proposes a solution to the above open problems using balanced multiwavelet-based image compression. The proposed scheme presents combination of two novel ideas: A coefficient reorganization suitable to balanced multiwavelet decomposition is used to regain the parent-child relationship. A block tree coding is used for compression and reconstruction of multiwavelet transformed image and thus the proposed scheme is called as Multiwavelet Block Tree Coding (MBTC). This approach yields the advantages of high energy compaction, PSNR, and a less number of bits for encoding. Balanced multiwavelet-based compression, with MBTC applied to a set of four mammographic images, achieves an average PSNR of 43.245 dB against the existing Set Partitioning In Hierarchical Trees (SPIHT) algorithm which on an average achieves only 34.181 dB for 0.5 bpp bit rate and MBTC requires encoding bits of 45.565% less than SPIHT encoding.

Set Partitioning in Hierarchical Trees (SPIHT) is an effective image compression algorithm. SPIHT is a unique approach which transmits the values of the pixels indirectly to the coordinates. The packet coders in the images present a universal transform the framework in a constriction of the filter bank schema which is attained by the Space Frequency Quantization (SFQ) and the effective compression is achieved by the Huffman coding. The proposed scheme permits transformation in the joints and alterations in the discrete magnitude of the input image. Hence, coding performance is enhanced and the image is compressed without compromising the excellence of the image. MSPIHT is an enhanced algorithm that fits the requirement of medical image compression for storage and transmission of medical data. MSPIHT improves on the standard SPIHT in the encoding operation of wavelet coefficients to provide high quality images at low bit rates important in medical applications where accuracy is paramount. The modification is aimed at changing the set partitioning strategy by minimizing memory consumption rates, thus improving the compression ratio and speed. It also may be used at telemedicine where high image compression rate is required for the quick and efficient image transfer for diagnostics and consultations. Experimental results shows that the image compression scheme is improved with the SPIHT-SFQ based Huffman Coding scheme and it outperforms the current image compression algorithms.

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.

The objective of this paper is to implement SPIHT and WDR image coder. We have used here two different images Natural and Artificial. Artificial image stand for image on which enhancement techniques have been applied e.g Wallpaper whereas Natural image is an image on which no enhancement technique has been applied e.g image captured by camera directly. SPIHT stands for Set Partitioning in Hierarchical Trees (SPIHT) Coding. The SPIHT coder is a highly refined version of the EZW algorithm and is a powerful image compression algorithm that produces an embedded bit stream from which the best reconstructed images in the mean square error sense can be extracted at various bit rates. Some of the best results -- highest PSNR values for given compression ratios for a wide variety of images have been obtained with SPIHT. Hence, it has become the benchmark state-of-the-art algorithm for image compression. One of the defects of SPIHT is that it only implicitly locates the position of significant coefficients. This makes it difficult to perform operations which depend on the position of significant transform values, such as region selection on compressed data. Region selection, also known as region of interest (ROI), means a portion of a compressed image that requires increased resolution. Such compressed data operations are possible with the WDR algorithm of Tian and Wells. The term difference reduction refers to the way in which WDR encodes the locations of significant wavelet transform values. Although WDR will not produce higher PSNR values than SPIHT. With the implementation of these algorithms we have calculated the various parameters e.g. CR, PSNR, BPP & MSE to analyze PQS (picture Quality Scale). We have analyzed our results using various wavelet filters such as Biorthogonal, Coif lets, Daubechies and Symlets etc.

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.

Wavelet-based image compression is proving to be a very effective technique for medical images, giving significantly better results than the JPEG algorithm. A novel scheme for encoding wavelet coefficients, termed set partitioning in hierarchical trees, has recently been proposed and yields significantly better compression than more standard methods. The authors report the results of experiments comparing such coding to more conventional wavelet compression and to JPEG compression on several types of medical images.

Set partitioning in hierarchical trees (SPIHT) image compression algorithm based on wavelet transform can't effectively express the image of the texture and contour. Contourlet transform can retain image details better because it avoids down sampling to high-frequency subband, and the coefficient is also sparser than it of wavelet transform. In this paper, a suitable SPIHT algorithm for contourlet transform was proposed for its coefficient matrix characteristics and distribution. The new algorithm not only overcome the disadvantage of not protecting details and texture effectively when using wavelet transform, but also saves coding time, at the same time enhance the image of the peak signal to noise ratio (PSNR). Simulation results show that the method is an efficient image compression algorithm. Its compression effect is obviously better than traditional SPIHT algorithms based on wavelet transform and WBCT (especially at low bit rates). It has generality.

Medical images must be compressed before transmission due to bandwidth and storage limitations. The set partitioning in hierar- chical trees (SPIHT) algorithm is an efficient method for lossy and loss- less coding of medical images. We propose some modifications to the SPIHT algorithm. It is based on the idea of the insignificant correlation of wavelet coefficients among medium- and high-frequency subbands. In this scheme, insignificant wavelet coefficients that correspond to the same spatial location in the medium subbands can be used to reduce the redundancy by a combined function proposed in associated with the modified SPIHT. In high-frequency subbands, the modified SPIHT pro- poses a dictator to reduce the interband redundancy. Experimental re- sults indicate that the proposed technique improves the quality of the reconstructed medical image in terms of both the peak signal-to-noise ratio (PSNR) and the perceptual results over JPEG2000 and the original SPIHT at the same bit rate. © 2003 Society of Photo-Optical Instrumentation Engi- neers. (DOI: 10.1117/1.1578645)

Recently many new algorithms for image compression based on wavelets have been developed.This paper gives a detailed explanation of SPIHT algorithm with the combination of Lempel Ziv Welch compression technique for image compression by MATLAB implementation. Set partitioning in Hierarchical trees (SPIHT) is one of the most efficient algorithm known today. Pyramid structures have been created by the SPIHT algorithm based on a wavelet decomposition of an image. Lempel Ziv Welch is a universal lossless data compression algorithm guarantees that the original information can be exactly reproduced from the compressed data.The proposed methods have better compression ratio, computational speed and good reconstruction quality of the image. To analysis the proposed lossless methods here, calculate the performance metrics as Compression ratio, Mean square error, Peak signal to Noise ratio. Key Words-LempelZivWelch (LZW),SPIHT,Wavelet