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In this paper we present a novel Joint Encryption and Compression (JEC) technique for transmission of biometric data over a wireless channel. The method offers advantages such as reduced data processing, security and enhanced recognition accuracy. The security of the biometric data is ensured by means of watermarking followed by random bit shuffling. The watermarking process involves embedding one׳s fingerprint information in his/her compressed face image. In order to compress face images face images wavelet based encoders such as SPIHT (Set Partitioning Hierarchical Trees) and JPEG2000 are used. The advantage of the proposed method is that the overall data rate can be minimized while simultaneously maintaining good quality reconstruction. The JEC (Joint Encryption and Compression) scheme is tested for its suitability in a wireless communication system. It can be seen that with the help of appropriate channel coding, high values of PSNR (Peak Signal to Noise Ratio) can be obtained. Consequently the recovered biometric data offers recognition rates that are found to be acceptable for a telebiometric system. Additionally an algorithm is suggested for fusion of individual biometric scores in order to improve the overall recognition performance. Simulation results depict that a significant advantage in verification rates can be achieved by this method. [ABSTRACT FROM AUTHOR]

The principal component analysis(PCA)method combined with JPEG2000 is widely used in hyperspectral image compression.However,the covariance matrix of the PCA only represents the second order statistics.In many applications of hyperspectral image analysis,only preserving the information of the second order statistics is not sufficient.Taking the anomalous pixels for example,more subtle information needs to be captured by using higher-order statistics.To solve this problem,we propose a hyperspectral image compression algorithm using mixed PCA/ICA in conjunction with JPEG2000 standard.Firstly,the PCA is performed for the original hyperspectral image to find the eigenvector matrix WPCA corresponding to the first mlargest eigenvalues.Then,the ICA is employed for the remaining eigenvectors to find neigenvector matrix WICA.Finally,the mixed projection matrix,original hyperspectral image and it’s mean vectors are embedded into the JPEG2000 bitstream for compression.At different bit rates,the performance of the mixed PCA/ICA+JPEG2000is evaluated by spatial correlation coefficient(ρ),signal noise ratio(SNR)and spectral angel map(SAM).Experimental results reveal that the proposed algorithm is not only better in spectral correlation reduction,but also can improve spectral fidelity and protect anomaly pixel information. [ABSTRACT FROM AUTHOR]

A novel lossless colour image compression scheme based on a reversible colour transform (RCT) and Burrows-Wheeler compression algorithm (BWCA) is presented. The lossless transformation from RGB to YUV colour space provides highly correlated pixel intensities in the transformed image, thus aiding in higher compression. The proposed scheme uses a two-pass Burrows-Wheeler transform (BWT) for the individual source image colour planes to enhance grey-level homogeneity in the 2-D space. Compression efficiency is compared against various schemes including the JPEG 2000 lossless compression scheme and the previously developed kernel move-to-front transform-based BWCA (kernel BWCA). Validation is carried out via small- and large-size images. The proposed method using RCT with bi-level BWT results in better compression by taking advantage of the redundancy in the grey levels brought by the YUV colour space. For small-size images, it achieves 45 and 126 per cent more compression than the JPEG2000 lossless and kernel BWCA scheme, respectively. Among the different schemes compared, the proposed scheme achieves overall best performance and is well suited to small- and large-size image data compression. [ABSTRACT FROM AUTHOR]

This paper studies the performance of hierarchical modulation-based multimedia transmission in cognitive radio (CR) systems with imperfect channel sensing results under constraints on both transmit and interference power levels. Unequal error protection (UEP) of data transmission using hierarchical quadrature amplitude modulation (HQAM) is considered in which high priority (HP) data is protected more than low priority (LP) data. In this setting, closed-form bit error rate (BER) expressions for HP data and LP data are derived in Nakagami-m fading channels in the presence of sensing errors. Subsequently, the optimal power control that minimizes weighted sum of average BERs of HP bits and LP bits or its upper bound subject to peak/average transmit power and average interference power constraints is derived and a low-complexity power control algorithm is proposed. Power levels are determined in three different scenarios, depending on the availability of perfect channel side information (CSI) of the transmission and interference links, statistical CSI of both links, or perfect CSI of the transmission link and imperfect CSI of the interference link. The impact of imperfect channel sensing decisions on the error rate performance of cognitive transmissions is also evaluated. In addition, tradeoffs between the number of retransmissions, the severity of fading, and peak signal-to-noise ratio (PSNR) quality are analyzed numerically. Moreover, performance comparisons of multimedia transmission with conventional quadrature amplitude modulation (QAM) and HQAM, and the proposed power control strategies are carried out in terms of the received data quality and number of retransmissions. [ABSTRACT FROM PUBLISHER]

A chaotic-cipher-based packet body encryption algorithm for JPEG2000 images is proposed in this paper. In order to produce a compliant codestream, a 2-byte block encryption procedure including bit-wise exclusive OR and cyclic rotation operations is investigated. Moreover, a repeatedly encryption process is adopted to eliminate the undesirable marker codes during encryption. The repeatedly encryption process only introduces a negligible computational overhead. Both simulations and analysis show that the proposed algorithm is of high security and simplicity. Therefore, these good properties make the algorithm efficient to process and useful for image applications. [ABSTRACT FROM AUTHOR]

Fast image codecs are a current need in applications that deal with large amounts of images. Graphics Processing Units (GPUs) are suitable processors to speed up most kinds of algorithms, especially when they allow fine-grain parallelism. Bitplane Coding with Parallel Coefficient processing (BPC-PaCo) is a recently proposed algorithm for the core stage of wavelet-based image codecs tailored for the highly parallel architectures of GPUs. This algorithm provides complexity scalability to allow faster execution at the expense of coding efficiency. Its main drawback is that the speedup and loss in image quality is controlled only roughly, resulting in visible distortion at low and medium rates. This paper addresses this issue by integrating techniques of visually lossless coding into BPC-PaCo. The resulting method minimizes the visual distortion introduced in the compressed file, obtaining higher-quality images to a human observer. Experimental results also indicate 12% speedups with respect to BPC-PaCo. [ABSTRACT FROM AUTHOR]

Finding optimal compression levels for diagnostic imaging is not an easy task. Significant compressibility variations exist between modalities, but little is known about compressibility variations within modalities. Moreover, compressibility is affected by acquisition parameters. In this study, we evaluate the compressibility of thousands of computed tomography (CT) slices acquired with different slice thicknesses, exposures, reconstruction filters, slice collimations, and pitches. We demonstrate that exposure, slice thickness, and reconstruction filters have a significant impact on image compressibility due to an increased high frequency content and a lower acquisition signal-to-noise ratio. We also show that compression ratio is not a good fidelity measure. Therefore, guidelines based on compression ratio should ideally be replaced with other compression measures better correlated with image fidelity. Value-of-interest (VOI) transformations also affect the perception of quality. We have studied the effect of value-of-interest transformation and found significant masking of artifacts when window is widened. [ABSTRACT FROM AUTHOR]

When working with the line-based wavelet transform, JPEG2000 needs to buffer many sub-band lines for future block coding. This coding buffer occupies the majority of the memory usage in a JPEG2000 system. In this paper, we first use the multilevel block-based wavelet transform, and then utilize the multilevel stripe-based wavelet transform to realize JPEG2000 coding. The system schemes of the multilevel block-based wavelet transform and the multilevel stripe-based wavelet transform for JPEG2000 are presented. The proposed schemes can effectively control the wavelet coefficient output pattern, and thus reduce the memory usage of JPEG2000 coding buffer. Compared with the line-based wavelet transform, the proposed schemes reduce more than 50% memory usage of the JPEG2000 system and slightly decrease the memory bandwidth. By holding the blocks/stripes in on-chip memory, which do not depend on the image size, the memory bandwidth of the JPEG2000 system can be further significantly reduced. The advantage of the stripe-based wavelet transform over the block-based wavelet transform lies in that it occupies much less internal memory. [ABSTRACT FROM AUTHOR]

The Arithmetic encoder (AE) is a throughput bottleneck of the entire JPEG2000 encoding system. But its sequential nature leads to a difficulty to improve its throughput. To overcome this bottleneck, a high-throughput AE capable of encoding one symbol per clock is proposed. First, the correlations between the sequential pairs of context and its decision are analyzed. Moreover, the burst number of CX and the burst number of the index value are studied. Due to these analyses, a conclusion is drawn in order to prevent the AE from stalling when the burst number of the context is larger than one. Based on this conclusion, an 'index-arbiter' method is introduced, which increased the throughput greatly. Furthermore, the probabilities of different adjusting manners for the interval register A and code register C before renormalization are given. On the basis of these data, a detector of leading-zero is proposed to simplify the renormalization procedure. Finally, the total number and the max burst number of the two-byte emission are given. According to these results, a FIFO size of 43*4 is introduced to prevent the pipeline from pause when two-byte emission occurs. The proposed architecture is synthesized with TSMC0.18 μm HS technology library of ARM Company. The implementation result shows that the throughput of the proposed architecture is 547Msymbols/s with an area of 79,012.84 μm. Compared with the paper Rhu and Park ( IEEE Transactions on Circuits and Systems for Video Technology, 20(3),446-451, 2010), the throughput has been increased by 37 %. [ABSTRACT FROM AUTHOR]

Recent lossless still image compression formats are powerful tools for compression of all kind of common images (pictures, text, schemes, etc.). Generally, the performance of a compression algorithm depends on its ability to anticipate the image function of the processed image. In other words, a compression algorithm to be successful, it has to take perfectly the advantage of coded image properties. Astronomical data form a special class of images and they have, among general image properties, also some specific characteristics which are unique. If a new coder is able to correctly use the knowledge of these special properties it should lead to its superior performance on this specific class of images at least in terms of the compression ratio. In this work, the novel lossless astronomical image data compression method will be presented. The achievable compression ratio of this new coder will be compared to theoretical lossless compression limit and also to the recent compression standards of the astronomy and general multimedia. [ABSTRACT FROM AUTHOR]

The new wide-field radio telescopes, such as: ASKAP, MWA, LOFAR, eVLA and SKA; will produce spectral-imaging data-cubes (SIDC) of unprecedented size—in the order of hundreds of Petabytes. Servicing such data as images to the end-user in a traditional manner and formats is likely going to encounter significant performance fallbacks. We discuss the requirements for extremely large SIDCs, and in this light we analyse the applicability of the approach taken in the JPEG2000 (ISO/IEC 15444) standards. We argue the case for the adaptation of contemporary industry standards and technologies versus the modification of legacy astronomy standards or development of new standards specific to astronomy only. [ABSTRACT FROM AUTHOR]

Remote sensing images often need to be coded and/or transmitted with constrained computational resources. Among other features, such images commonly have high spatial, spectral, and bit-depth resolution, which may render difficult their handling. This letter introduces an embedded quantization scheme based on two-step scalar deadzone quantization (2SDQ) that enhances the quality of transmitted images when coded with a constrained number of bits. The proposed scheme is devised for use in JPEG2000. It is named cell-based 2SDQ since it uses cells, i.e., small sets of wavelet coefficients within the codeblocks defined by JPEG2000. Cells permit a finer discrimination of coefficients in which to apply the proposed quantizer. Experimental results indicate that the proposed scheme is especially beneficial for high bit-depth hyperspectral images. [ABSTRACT FROM PUBLISHER]

Common image compression techniques suitable for general purpose may be less effective for such specific applications as video surveillance. Since a stationed surveillance camera always targets at a fixed scene, its captured images exhibit high consistency in content or structure. In this paper, we propose a surveillance image compression technique via dictionary learning to fully exploit the constant characteristics of a target scene. This method transforms images over sparsely tailored over-complete dictionaries learned directly from image samples rather than a fixed one, and thus can approximate an image with fewer coefficients. A set of dictionaries trained off-line is applied for sparse representation. An adaptive image blocking method is developed so that the encoder can represent an image in a texture-aware way. Experimental results show that the proposed algorithm significantly outperforms JPEG and JPEG 2000 in terms of both quality of reconstructed images and compression ratio as well. [ABSTRACT FROM AUTHOR]

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Although it was expected that multispectral images would be implemented in many applications, such as remote sensing and medical imaging, their use has not been widely diffused in these fields. The development of a compact multispectral camera and display will be needed for practical use, but the format compatibility between multispectral and RGB images is also important for reducing the introduction cost and having high usability. We propose a method of embedding the spectral information into an RGB image by watermarking. The RGB image is calculated from the multispectral image, and then, the original multispectral image is estimated from the RGB image using Wiener estimation. The residual data between the original and the estimated multispectral image are compressed and embedded in the lower bit planes of the RGB image. The experimental results show that, as compared with Wiener estimation, the proposed method leads to more than a 10 dB gain in the peak signal-to-noise ratio of the reconstructed multispectral image, while there are almost no significant perceptual differences in the watermarked RGB image. [ABSTRACT FROM AUTHOR]

We propose a new method for JPEG2000 region of interest (ROI) coding called the background pretruncated method (BPTM). An algorithm to calculate the key parameter in the BPTM is also proposed. The published JPEG2000 ROI coding methods have difficulty in balancing ROI quality and system computation. Methods based on shifting coefficients add much computation, and they significantly penalize the coding efficiency. Implicit methods cannot obtain as high-ROI quality as the coefficient shift methods. To efficiently achieve ROI coding, the BPTM assigns a proper number of the least significant bitplanes of the background to zero, and it can not only achieve high-ROI quality but also add no computation to the other modules in JPEG2000. Additionally, the BPTM supports arbitrary shaped ROI coding, and the codestream is compatible with JPEG2000 standard. [ABSTRACT FROM AUTHOR]

Real integration of Virtual Microscopy with the pathologist service workflow requires the design of adaptable strategies for any hospital service to interact with a set of Whole Slide Images. Nowadays, mobile devices have the actual potential of supporting an online pervasive network of specialists working together. However, such devices are still very limited. This article introduces a novel highly adaptable strategy for streaming and visualizing WSI from mobile devices. The presented approach effectively exploits and extends the granularity of the JPEG2000 standard and integrates it with different strategies to achieve a lossless, loosely-coupled, decoder and platform independent implementation, adaptable to any interaction model. The performance was evaluated by two expert pathologists interacting with a set of 20 virtual slides. The method efficiently uses the available device resources: the memory usage did not exceed a 7% of the device capacity while the decoding times were smaller than the 200 ms per Region of Interest, i.e., a window of 256 × 256 pixels. This model is easily adaptable to other medical imaging scenarios. [ABSTRACT FROM AUTHOR]

Ship detection on spaceborne images has attracted great interest in the applications of maritime security and traffic control. Optical images stand out from other remote sensing images in object detection due to their higher resolution and more visualized contents. However, most of the popular techniques for ship detection from optical spaceborne images have two shortcomings: 1) Compared with infrared and synthetic aperture radar images, their results are affected by weather conditions, like clouds and ocean waves, and 2) the higher resolution results in larger data volume, which makes processing more difficult. Most of the previous works mainly focus on solving the first problem by improving segmentation or classification with complicated algorithms. These methods face difficulty in efficiently balancing performance and complexity. In this paper, we propose a ship detection approach to solving the aforementioned two issues using wavelet coefficients extracted from JPEG2000 compressed domain combined with deep neural network (DNN) and extreme learning machine (ELM). Compressed domain is adopted for fast ship candidate extraction, DNN is exploited for high-level feature representation and classification, and ELM is used for efficient feature pooling and decision making. Extensive experiments demonstrate that, in comparison with the existing relevant state-of-the-art approaches, the proposed method requires less detection time and achieves higher detection accuracy. [ABSTRACT FROM PUBLISHER]

As high-resolution digital images that are used in remote sensing technologies and medical imaging, tend to be of large sizes and thereby consuming large storage space, large transmission bandwidth, and long transmission times. Therefore, image compression is required before storage and transmission. JPEG2000 and JPEG is the widely used compression standard offering best compression performance. However, compression leads to loss of data and may lead to erroneous results. Thus, there is a need for image quality assessment (IQA) of compressed images at various compression stages. In this paper, we address the full-reference (FR) image quality metric (IQM) for JPEG compressed images and we present a new effective and efficient IQA model, called LSDBIQ (local standard deviation based image quality). The approach is based on the comparison of the local standard deviation of two images. The proposed metrics is tested on four well-known databases available in the literature (TID2013, TID2008, LIVE and CSIQ). Experimental results show that the proposed metrics outperforms other models for the assessment of image quality and have very low computational complexity. [ABSTRACT FROM AUTHOR]

A methodology for visually lossless compression of monochrome stereoscopic 3D images is proposed. Visibility thresholds are measured for quantization distortion in JPEG2000. These thresholds are found to be functions of not only spatial frequency, but also of wavelet coefficient variance, as well as the gray level in both the left and right images. To avoid a daunting number of measurements during subjective experiments, a model for visibility thresholds is developed. The left image and right image of a stereo pair are then compressed jointly using the visibility thresholds obtained from the proposed model to ensure that quantization errors in each image are imperceptible to both eyes. This methodology is then demonstrated via a particular 3D stereoscopic display system with an associated viewing condition. The resulting images are visually lossless when displayed individually as 2D images, and also when displayed in stereoscopic 3D mode. [ABSTRACT FROM AUTHOR]

Region of interest (ROI) based image compression can offer a high image-compression ratio along with high quality in the important regions of the image. For many applications, stable compression quality is required for both the ROIs and the images. However, image compression does not consider information specific to the application and cannot meet this requirement well. This paper proposes an application-oriented ROI-based image-compression method using bit-allocation optimization. Unlike typical methods that define bitrate parameters empirically, the proposed method adjusts the bit-rate parameters adaptively to both images and ROIs. First, an application-dependent optimization model is constructed. The relationship between the compression parameters and the image content is learned from a training image set. Image redundancy is used to measure the compression capability of image content. Then, during compression, the global bit rate and the ROI bit rate are adjusted in the images and ROIs, respectively-supported by the application-dependent information in the optimization model. As a result, stable compression quality is assured in the applications. Experiments with two different applications showed that quality deviation in the reconstructed images decreased, verifying the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Entropy is a measure of a message uncertainty. Among others aspects, it serves to determine the minimum coding rate that practical systems may attain. This paper defines an entropy-based measure to evaluate context models employed in wavelet-based image coding. The proposed measure is defined considering the mechanisms utilized by modern coding systems. It establishes the maximum performance achievable with each context model. This helps to determine the adequateness of the model under different coding conditions and serves to predict with high precision the coding rate achieved by practical systems. Experimental results evaluate four well-known context models using different types of images, coding rates, and transform strategies. They reveal that, under specific coding conditions, some widely-spread context models may not be as adequate as it is generally thought. The hints provided by this analysis may help to design simpler and more efficient wavelet-based image codecs. [ABSTRACT FROM AUTHOR]