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A multi-level 2D Discrete wavelet transform (DWT) architecture for JPEG2000 is proposed, enhancing speed through parallel processing multiple tile blocks. Based on the lifting scheme, folded architecture and unfolded architecture achieving critical path delay with only one multiplier are designed to increase throughput rate. Connecting the folded and unfolded architecture through a pipeline architecture ensures uniform throughput rates across all DWT levels within a singular clock domain. Computational resource consumption is reduced by adjusting the timing to allow one folded architecture to process three tile blocks of three to five levels of DWT, and a transposing module requiring merely six registers is devised to decrease storage resource consumption. The quantization module, crucial for code-word control in JPEG2000, is integrated into the scaling module with minimal additional resource expenditure. Compared to the existing architecture, the analysis demonstrates that the proposed architecture exhibits enhanced hardware efficiency, with a reduction in transistor-delay-product (TDP) of no less than 14.69%. Synthesis results further reveal an area reduction of at least 26.64%, and a decrease in area-delay-product (ADP) by a minimum of 29.89%. Results from FPGA implementation indicate a significant decrease in resource utilization. [ABSTRACT FROM AUTHOR]

Due to the extensive use of multimedia technologies, there is a pressing need for advancements and enhanced efficiency in picture compression. JPEG 2000 standard aims to meet the needs for encoding still pictures. JPEG 2000 is an internationally recognized standard for compressing still images. It provides a wide range of features and offers superior compression ratios and interesting possibilities when compared to traditional JPEG approaches. Nevertheless, the MQ decoder in the JPEG 2000 standard presents a substantial obstacle for real-time applications. In order to fulfill the demands of real-time processing, it is imperative to meticulously devise a high-speed MQ decoder architecture. This work presents a novel MQ decoder architecture that is both high-speed and area-efficient, making it comparable to previous designs and well-suited for chip implementation. The design is implemented using the VHDL hardware description language and is synthesized with Xilinx ISE 14.7 and Vivado 2015.1. The implementation findings show that the design functions at a frequency of 438.5 MHz on Virtex-6 and 757.5 MHz on Zync7000. For these particular frequencies, the calculated frame rate is 63.1 frames per second. [ABSTRACT FROM AUTHOR]

Lossless hyperspectral images have the advantage of reducing the data size, hence saving on storage and transmission costs. This study presents a dynamic pipeline hardware design for compressing and decompressing images using the Joint Photographic Experts Group-Lossless (JPEG2000) algorithm. The proposed architecture was specifically tailored for implementation on a Field Programmable Gate Array (FPGA) to accomplish efficient image processing. The introduction of a pipeline pause mechanism effectively resolves the issue of coding errors deriving from parameter modifications. Bit-plane coding was employed to enhance the efficacy of image coding calculations, leading to a reduction of parameter update delays. However, the context and decision creation procedure were streamlined, resulting in a significant enhancement in throughput. A hardware module utilizing the parallel block compression architecture was developed for JPEG2000 compression/decompression, allowing for configurable block size and bringing about enhanced image, compression/decompression, throughput, and reduced times. Verification results were obtained by implementing the proposed JPEG 2000 compression on a Zynq-7000 system-on-chip. The purpose of this system was to enable on-board satellite processing of hyperspectral image cubes with a specific focus on achieving lossless compression. The proposed architecture outperformed previous approaches by using fewer resources and achieving a higher compression ratio and clock frequency. [ABSTRACT FROM AUTHOR]

This paper explores learned image compression based on traditional and learned discrete wavelet transform (DWT) architectures and learned entropy models for coding DWT subband coefficients. A learned DWT is obtained through the lifting scheme with learned nonlinear predict and update filters. Several learned entropy models, with varying computational complexities, are explored to exploit inter- and intra-DWT subband coefficient dependencies, akin to traditional EZW, SPIHT, or EBCOT algorithms. Experimental results show that when the explored learned entropy models are combined with traditional wavelet filters, such as the CDF 9/7 filters, compression performance that far exceeds that of JPEG2000 can be achieved. When the learned entropy models are combined with the learned DWT, compression performance increases further. The computations in the learned DWT and all entropy models, except one, can be simply parallelized, and thus, the systems provide practical encoding and decoding times on GPUs, unlike other DWT-based learned compression systems in the literature. [ABSTRACT FROM AUTHOR]

In today’s highly computerized world, data compression is a key issue to minimize the costs associated with data storage and transfer. In 2019, more than 70% of the data sent over the network were images. This paper analyses the feasibility of using the SVD algorithm in image compression and shows that it improves the efficiency of JPEG and JPEG2000 compression. Image matrices were decomposed using the SVD algorithm before compression. It has also been shown that as the image dimensions increase, the fraction of eigenvalues that must be used to reconstruct the image in good quality decreases. The study was carried out on a large and diverse set of images, more than 2500 images were examined. The results were analyzed based on criteria typical for the evaluation of numerical algorithms operating on matrices and image compression: compression ratio, size of compressed file, MSE, number of bad pixels, complexity, numerical stability, easiness of implementation

A method of adaptive quantization for the coefficients of a discrete wavelet transformation in the JPEG2000 standard is proposed on the basis of an algorithm for the search and identification of fine image structures distinguishable by human vision. A method for the classification of image blocks by structural features and an algorithm for the identification of block transformation coefficients within the subranges of a discrete wavelet transformation are described. The adaptive quantization algorithm is described, and some recommendations on the adjustment of quantizer parameters to a high image sharpness are given. Some experimental estimates are given for the efficiency of the compression of photorealistic images with different degrees of details to result in the conclusions about the advantages of the new method over its analogues with recommendations on its further development in intelligent video systems. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering Sciences & Information Technology is the property of Arab Journal of Sciences & Research Publishing (AJSRP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years images have been used widely by online social networks providers or numerous organizations such as governments, police departments, colleges, universities, and private companies. It held in vast databases. Thus, efficient storage of such images is advantageous and its compression is an appealing application. Image compression generally represents the significant image information compactly with a smaller size of bytes while insignificant image information (redundancy) already been removed for this reason image compression has an important role in data transfer and storage especially due to the data explosion that is increasing significantly. It is a challenging task since there are highly complex unknown correlations between the pixels. As a result, it is hard to find and recover a well-compressed representation for images, and it also hard to design and test networks that are able to recover images successfully in a lossless or lossy way. Several neural networks and deep learning methods have been used to compress images. This article survey most common techniques and methods of image compression focusing on auto-encoder of deep learning. [ABSTRACT FROM AUTHOR]

The article proposes a method for controlling the transmission quality of fine image details in the JPEG2000 standard based on an automatic adjustment of the quantization parameters of discrete wavelet transform (DWT) coefficients. An algorithm for setting up quantization scale parameters for different transformation sub-ranges of the DWT coefficients depending on given (permissible) distortions is described. For an objective assessment of image quality, numerical measures of fine detail distortion in a normalized N-CIELAB colorimetric system are used, according to which an analysis of their structural features is performed. Results of the experimental studies of the analysis of image quality and compression efficiency depending on the quantization parameters utilized in the developed adaptive compression algorithm are presented. Results of evaluating the algorithm performance that can be used for practical applications in multimedia applications are also presented.

Modern image and video compression standards employ computationally intensive algorithms that provide advanced features to the coding system. Current standards often need to be implemented in hardware or using expensive solutions to meet the real-time requirements of some environments. Contrarily to this trend, this paper proposes an end-to-end codec architecture running on inexpensive Graphics Processing Units (GPUs) that is based on, though not compatible with, the JPEG2000 international standard for image and video compression. When executed in a commodity Nvidia GPU, it achieves real time processing of 12K video. The proposed S/W architecture utilizes four CUDA kernels that minimize memory transfers, use registers instead of shared memory, and employ a double-buffer strategy to optimize the streaming of data. The analysis of throughput indicates that the proposed codec yields results at least 10× superior on average to those achieved with JPEG2000 implementations devised for CPUs, and approximately 4× superior to those achieved with hardwired solutions of the HEVC/H.265 video compression standard.

Effective compression of hyperspectral (HS) images is essential due to their large data volume. Since these images are high dimensional, processing them is also another challenging issue. In this work, an efficient lossy HS image compression method based on enhanced multivariance products representation (EMPR) is proposed. As an efficient data decomposition method, EMPR enables us to represent the given multidimensional data with lower-dimensional entities. EMPR, as a finite expansion with relevant approximations, can be acquired by truncating this expansion at certain levels. Thus, EMPR can be utilized as a highly effective lossy compression algorithm for hyper spectral images. In addition to these, an efficient variety of EMPR is also introduced in this article, in order to increase the compression efficiency. The results are benchmarked with several state-of-the-art lossy compression methods. It is observed that both higher peak signal-to-noise ratio values and improved classification accuracy are achieved from EMPR-based methods. [ABSTRACT FROM AUTHOR]

The increasing performances of personal computers, as well as software of image processing, enable the easy manipulation of digital media content. Unfortunately, this easy manipulation makes the detection of changes in the multimedia content a very difficult task. Digital watermarking is among the most appropriate techniques to verify the integrity of multimedia content. In this paper, we propose a semi-fragile watermarking scheme for JPEG2000 image self-authentication that ensuring the robustness of the watermark against the compression attacks generated by the JPEG2000 encoder itself. The scheme is combined with the JPEG2000 encoder by embedding the generated watermark into the host image during the JPEG2000 compression process. To generate the watermark we suppose to use a perceptual hash function (PHF) operating on discrete wavelet coefficients of the host image. The proposed watermark generation process leads the system to verify the integrity of the image without the need to any file except for the watermarked image. The watermark is embedded during the JPEG2000 compression process after Discrete Wavelet Transform (DWT) step into the approximation sub-band coefficients of the five wavelet decomposition using Index Modulation Quantification (QIM), and can be extracted during image decoding. To prove the authentication of the image, the system compares the extracted watermark with the new watermark generated from the received image. Experimental results show that our proposed approach has not only an extremely high accuracy of tampering detection but also a relatively very high resistance against JPEG2000 compression attacks. [ABSTRACT FROM AUTHOR]

Aiming at the problem that remote sensing image resolution continues to increase and transmission channel bandwidth limit, a high frame rate image coding device for FPGA-controlled dedicated JPEG2000 standard algorithm integrated circuit is proposed. The original image data is received through the LVDS interface, and then transmitted to the compression coding module after being pinged by the ping-pong, which also greatly increases the transmission rate. After actual testing, the device achieves high compression ratio requirements for high frame rate images, reduces the bandwidth requirements of the transmission channel, and is stable and reliable.

Although the JPEG2000 compression standard has high coding efficiency,its error resistance and security can’t meet the requirements of practical application.Based on this,a fast bidirectionally-decodable arithmetic coding method with chaotic redundancy and threshold control was proposed.At the encoder,the chaotic map controlled the probabilities of multiple redundant symbols to enhance the security of arithmetic coding.At the decoder,threshold control and bidirectional decoding were combined to realize fast decoding based on maximum a posteriori estimation.Simulation results show that the proposed method improves the reconstructed image quality with better error resistance and security.

We present an end-to-end image compression system based on compressive sensing. The presented system integrates the conventional scheme of compressive sampling (on the entire image) and reconstruction with quantization and entropy coding. The compression performance, in terms of decoded image quality versus data rate, is shown to be comparable with JPEG and significantly better at the low rate range. We study the parameters that influence the system performance, including (i) the choice of sensing matrix, (ii) the trade-off between quantization and compression ratio, and (iii) the reconstruction algorithms. We propose an effective method to select, among all possible combinations of quantization step and compression ratio, the ones that yield the near-best quality at any given bit rate. Furthermore, our proposed image compression system can be directly used in the compressive sensing camera, e.g., the single pixel camera, to construct a hardware compressive sampling system. [ABSTRACT FROM AUTHOR]

This paper proposes a regional rate allocation method for enhancing the perceived quality in image compression. Bit allocation to image regions should be performed by considering the viewer's attention and distortion sensitivity maps in order to address subjective quality concerns. The paper first proposes an exponential model for the relation between the viewer's fixation duration and perceived information. The human visual system is more sensitive to the distortion around edges than the distortion in complex textured regions. Therefore, a novel distortion sensitivity method is also proposed that distinguishes true edges from complex textures without using edge detectors or gradient magnitude thresholds. The estimates for the visual attention level and the distortion sensitivity level are jointly used to modify the distortion contribution of each codeblock for determining its quantization parameter. The experiments validate the improved perceptual quality of decoded images due to the integrated use of the visual distortion sensitivity and the visual attention level in bit allocation. Moreover, the proposed bit allocation method is experimentally shown to yield a substantially higher subjective evaluation score than the other well-known bit allocation methods based on post-compression rate-distortion optimization, saliency maps, foveation of fixations and foveated just-noticeable-difference maps. [ABSTRACT FROM AUTHOR]

Semi-fragile watermarking technique for digital image, as a technology for content authentication, aims at telling malicious tampering from content-preserving operators. However, with the resolution of imaging sensors increasing and the explosive growth of digital images on the internet, before authorization, watermarked images tend to undergo such double-compression environments as: first compression (JPEG/JPEG2000) for release, decoding, application processing (conventional signal processing such as nosing, filtering, cropping, and scaling/security attack/malicious tampering), and second compression (JPEG2000/JPEG) for release again. In this paper, based on rotating vector, we propose a novel watermarking expression method that can describe carrier semantics to a certain extent and its modulation algorithm for digital image, and analyze the stability of the watermarked data theoretically. Then, a semi-fragile watermarking scheme is proposed for image content authentication to effectively distinguish malicious content manipulation from content-preserving operations in the double-compression application environment, which expands the application scope of content authentication based on watermarking technology. [ABSTRACT FROM AUTHOR]

In this paper, a new robust reversible data hiding method is proposed. The method is designed based on wavelet modifications which result in a scalable data hiding scheme. The well-known biorthogonal wavelets are modified according to the watermarking bits. This is done in a way that the embedded bit can easily be interpreted based on the wavelet coefficients of the watermarked image and regardless of its resolution. Following such an algorithm would result in both reversibility and robustness. The proposed method is especially robust against wavelet resolution changing attacks and DWT based compressions. This can be of high value when dealing with low bandwidth communication situations. The practical results show high robustness against signal processing attacks and high PSNR and capacity in lossless scenarios. [ABSTRACT FROM AUTHOR]

Embedded Block Coding with Optimized Truncation (EBCOT) used in JPEG2000 standard performs the sequential processing of code blocks for clear images. The employment of Matrix Quantizer (MQ) in EBCOT reduces the computational burden by generating the context decision pair from probabilistic estimation. But, it consumes huge devices and occupies large memory space that leads to the difficulties in hardware design. The bit truncation in MQ-coder reduces the computational complexities that lead to a reduction in the memory space occupation in the hardware platform. This paper proposes the Auxiliary Minutest MQ (AMMQ) architecture for bit truncation in the probability estimation of context decision pair making. The reduction in a number of bits minimizes the slice registers and lookup tables utilization that offers less flip-flop consumption to achieve the compact chip development for JPEG2000 compression. Besides, the reduction in computational steps effectively minimizes the time delay and hence the high operating frequency. The comparative analysis of proposed AMMQ-coder architecture with the existing architectures regarding the memory assures the effectiveness of new way of bit truncation scheme in the JPEG2000 compression. [ABSTRACT FROM AUTHOR]

This paper is dedicated to a hardware architecture development for JPEG2000 entropy decoding unit (EDU) which consist of two parts: context modeling unit (CMU) and arithmetic decoding unit (ADU). To achieve a high throughput we propose an efficient pixel skipping scheme to save clock cycles for non-context position in CMU and realize the ADU logic by combinational circuit to keep the result of ADU within the same clock of context which can reduce the latency to zero clock cycle between CMU and ADU. We show that the proposed EDU architecture attains a throughput of 68.66–232.14 Mbps for a single decoding core depending on compression ratios, and consumes 95.79 mW based on SMIC 0.13 um technology. In comparison with the state-of-the-art decoders the proposed EDU architecture provides consistent reconstruction performance with software decoder and comparable throughput, memory and power consumption. [ABSTRACT FROM AUTHOR]

Embedded zerotree wavelet (EZW) algorithm is the well‐known effective coding technique for low‐bit‐rate image compression. In this study, the authors propose a modification of this algorithm, namely new enhanced EZW (NE‐EZW), allowing to achieve a high compression performance in terms of peak‐signal‐to‐noise ratio and bitrate for lossy image compression. To distribute probabilities in a more efficient way, the proposed approach is based on increasing the number of coefficients not to be encoded by the use of new symbols. Furthermore, the proposed method optimises the binary coding by the use of the compressor cell operator. Experimental results demonstrated the effectiveness of the proposed scheme over the conventional EZW and other improved EZW schemes for both natural and medical image coding applications. They have also shown that the proposed approach outperforms the most well‐known algorithms, namely set partitioning in hierarchical trees (SPIHT) and JPEG2000. [ABSTRACT FROM AUTHOR]

The progressive nature of the JPEG2000 coded bitstream allows the reconstruction of images of different qualities from a single coded bitstream. This feature is utilized in this work to estimate the mean-squared-error (MSE) of reconstructed images without requiring the original image. It is based on the fact that if the MSE between the original image and a lower quality image is known, the MSE for higher quality images can be estimated from a quality scalable bitstream. The proposed method is highly accurate and is very simple as no complex statistical modeling is needed. Therefore, it is suitable to measure the fidelity of JPEG2000 decoded images at any desired quality in a real-time scenario. [ABSTRACT FROM AUTHOR]

Binary MQ arithmetic coding is widely used as a basic entropy coder in multimedia coding system. MQ coder esteems high in compression efficiency to be used in JBIG2 and JPEG2000. The importance of arithmetic coding is increasing after it is adopted as a unique entropy coder in HEVC standard. In the binary MQ coder, arithmetic approximation without multiplication is used in the process of recursive subdivision of range interval. Because of the MPS/LPS exchange activity that happens in the MQ coder, the output byte tends to increase. This paper proposes an enhanced binary MQ arithmetic coder to make use of look-up table (LUT) for (A × Qe) using quantization skill to improve the coding efficiency. Multi-level quantization using 2-level, 4-level and 8-level look-up tables is proposed in this paper. Experimental results applying to binary documents show about 3% improvement for basic context-free binary arithmetic coding. In the case of JBIG2 bi-level image compression standard, compression efficiency improved about 0.9%. In addition, in the case of lossless JPEG2000 compression, compressed byte decreases 1.5% using 8-level LUT. For the lossy JPEG2000 coding, this figure is a little lower, about 0.3% improvement of PSNR at the same rate.

Recently, convolutional neural networks have been successfully applied to lossy image compression. End-to-end optimized autoencoders, possibly variational, are able to dramatically outperform traditional transform coding schemes in terms of rate-distortion trade-off; however, this is at the cost of a higher computational complexity. An intensive training step on huge databases allows autoencoders to learn jointly the image representation and its probability distribution, possibly using a non-parametric density model or a hyperprior auxiliary autoencoder to eliminate the need for prior knowledge. However, in the context of on board satellite compression, time and memory complexities are submitted to strong constraints. The aim of this paper is to design a complexity-reduced variational autoencoder in order to meet these constraints while maintaining the performance. Apart from a network dimension reduction that systematically targets each parameter of the analysis and synthesis transforms, we propose a simplified entropy model that preserves the adaptability to the input image. Indeed, a statistical analysis performed on satellite images shows that the Laplacian distribution fits most features of their representation. A complex non parametric distribution fitting or a cumbersome hyperprior auxiliary autoencoder can thus be replaced by a simple parametric estimation. The proposed complexity-reduced autoencoder outperforms the Consultative Committee for Space Data Systems standard (CCSDS 122.0-B) while maintaining a competitive performance, in terms of rate-distortion trade-off, in comparison with the state-of-the-art learned image compression schemes.

Hyperspectral images offer great possibilities for remote studies, but can be difficult to manage due to their size. Compression helps with storage and transmission, and many efforts have been made towards standardizing compression algorithms, especially in the lossless and near-lossless domains. For long term storage, lossy compression is also of interest, but its complexity has kept it away from real-time performance. In this paper, JYPEC, a lossy hyperspectral compression algorithm that combines PCA and JPEG2000, is accelerated using an FPGA. A tier 1 coder (a key step and the most time-consuming in JPEG2000 compression) was implemented in a heavily pipelined fashion. Results showed a performance comparable to that of existing 0.18 μm CMOS implementations, all while keeping a small footprint on FPGA resources. This enabled the acceleration of the most complex step of JYPEC, bringing the total execution time below the real-time constraint.

Constant increasing of visual information necessitates most efficient image compression schemes for saving storage space or reducing required transmission bandwidth. In compressing a class of images, such as a fingerprint database, facial images of an organization or MR images of a hospital, overall information redundancy is increased and compression becomes more significant. In this paper, image signal sparse representation and RLS-DLA dictionary design are utilized for compressing whole or part of a facial image database by exploiting the structural similarity of the class members. In the proposed algorithm, images are compressed by multiple overcomplete learned dictionaries which are designed to provide least required bit-rates for different target qualities. To fortify the process, more interested head and shoulders regions of the images are extracted to provide dictionary training sets. A combined edge detection and active contour segmentation method is used for a robust ROI extraction. Simulation results show superior performance of about 0.3 to 1.5 dB quality enhancement in terms of PSNR, for similar compression ratios compared to JPEG2000 standard for the complete image, and a near loss-less compression for restoring the ROI. [ABSTRACT FROM AUTHOR]

The conventional strategy to download images captured by satellites is to compress the data on board and then transmit them via the downlink. It often happens that the capacity of the downlink is too small to accommodate all the acquired data, so the images are trimmed and/or transmitted through lossy regimes. This paper introduces a coding system that increases the amount and quality of the downloaded imaging data. The main insight of this paper is to use both the uplink and the downlink to code the images. The uplink is employed to send reference information to the satellite so that the onboard coding system can achieve higher efficiency. This reference information is computed on the ground, possibly employing extensive data and computational resources. The proposed system is called dual link image coding. As it is devised in this paper, it is suitable for Earth observation satellites with polar orbits. Experimental results obtained for data sets acquired by the Landsat 8 satellite indicate significant coding gains with respect to conventional methods. [ABSTRACT FROM AUTHOR]

The bandwidth of the communication networks has been increased continuously as results of technological advances. However, the introduction of new services and the expansion of the existing ones have resulted in even higher demand for the bandwidth. This explains the many efforts currently being invested in the area of data compression. The primary goal of these works is to develop techniques of coding information sources such as speech, image and video to reduce the number of bits required to represent a source without significantly degrading its quality. With the large increase in the generation of digital image data, there has been a correspondingly large increase in research activity in the field of image compression. The goal is to represent an image in the fewest number of bits without losing the essential information content within . Images carry three main type of information: redundant, irrelevant , and useful . Redundant information is the deterministic part of the information, which can be reproduced without loss from other information contained in the image. Irrelevant information is the part of information that has enormous details, which are beyond the limit of perceptual significance (i.e., psychovisual redundancy). Useful information, on the other hand, is the part of information, which is neither redundant nor irrelevant. Human usually observes decompressed images. Therefore, their fidelities are subject to the capabilities and limitations of the Human Visual System. This paper provides a survey on various image compression techniques, their limitations, compression rates and highlights current research in medical image compression. [ABSTRACT FROM AUTHOR]

Among the existing image coding methods, set partition in hierarchy tree (SPIHT) becomes a favourable choice for the energy‐constrained embedded computing system because of its simplicity and high coding efficiency. In this study, the authors presented a quad‐tree search model which is able to provide the higher coding efficiency than the model used in SPIHT. By applying this model, the authors proposed a new image codec, which is able to surpass SPIHT by 0.2–0.5 dB in peak signal‐to‐noise ratio over various code rates, and its performance is even comparable to SPIHT with adaptive arithmetic code and JPEG2000. Also an experiment is conducted to show the complexity of the proposed codec is the same as SPIHT without the complicated arithmetic codes. This property is critically favoured in embedded processing communication systems where energy consumption and speed are priority concerns. [ABSTRACT FROM AUTHOR]

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As one of the most studied areas, telemedicine allows doctors and pathologists to share their intelligence all over the world. To achieve the highest efficiency of remote pathology image browsing, especially for the whole-slide pathology image (WSI) big data, we develop a method to conduct the image coding based on the human visual quality for multiple displaying resolutions. In this paper, we establish a multi-resolutional human visual sensitivity model of Wavelet transform coefficients, based on the JPEG2000 standard and the human visual quality characters. With the analysis of the visual masking effect, we further propose a visibility threshold (VT) model for the human visual sensitivity optimized image coding of the WSIs, which considers multi-resolution and different visual qualities jointly. The proposed image coding methods with multiple quality or resolution layers can also be implemented for progressive image transmission and decoding. Compared with the conventional MSE optimized image coding algorithm, the decoded pathology image quality obtained from certain amounts of coded data using the proposed algorithm was proved to be improved from the aspect of visual perception through both objective and subjective evaluations. The perception-optimized progressive pathology image transmission and decoding were also verified with the combination of the proposed VT model and the JPIP protocol. With the proposed method, a remote medical server can adaptively transmit the image data according to the client's subjective requirements for the remote browsing of pathology image big data, leading to improved efficiency of diagnosis in telemedicine. • The multi-resolutional and multi-perceptional image coding scheme is proposed. • The visual masking model is proposed especially for whole-slide pathology images. • The progressive coding, transmission and decoding are also enabled. • Our method is shown to outperform the conventional MSE based method. [ABSTRACT FROM AUTHOR]

Since lossless compression can only achieve two to four times data compression, it may not be efficient to deploy lossless compression in bandwidth constrained applications. Instead, it would be more economical to adopt perceptually lossless compression, which can attain ten times or more compression without loss of important information. Consequently, one can transmit more images over bandwidth limited channels. In this research, we first aimed to compare and select the best compression algorithm in the literature to achieve a compression ratio of 0.1 and 40 dBs or more in terms of a performance metric known as human visual system model (HVSm) for maritime and sonar images. Our second objective was to demonstrate error concealment algorithms that can handle corrupted pixels due to transmission errors in interference-prone communication channels. Using four state-of-the-art codecs, we demonstrated that perceptually lossless compression can be achieved for realistic maritime and sonar images. At the same time, we also selected the best codec for this purpose using four performance metrics. Finally, error concealment was demonstrated to be useful in recovering lost pixels due to transmission errors.

With the increasing utilization of medical imaging in clinical practice and the growing dimensions of data volumes generated by various medical imaging modalities, the distribution, storage, and management of digital medical image data sets requires data compression. Over the past few decades, several image compression standards have been proposed by international standardization organizations. This paper discusses the current status of these image compression standards in medical imaging applications together with some of the legal and regulatory issues surrounding the use of compression in medical settings. [ABSTRACT FROM AUTHOR]

In this paper, data hiding algorithm using Discrete Wavelet Transform (DWT) and Arnold Transform is proposed. The secret data is scrambled using Arnold Transform to make it secure. Wavelet subbands of a cover image are obtained using DWT. The scrambled secret data is embedded into significant wavelet coefficients of subbands of a cover image. The proposed algorithm is robust to a variety of attacks like JPEG and JPEG2000 compression, image cropping and median filtering. Experimental results show that the PSNR of the composite image is 1.05 dB higher than the PSNR of existing algorithms and capacity is 25% higher than the capacity of existing algorithms. [ABSTRACT FROM AUTHOR]

Context-based compression plays a vital role in digital communication systems, since a particular region alone can be preserved using high bit rate and the other regions can be compressed using low bit rate compressions. Such methods are of great interest in tele-radiology applications requiring large storage. This paper presents an enhanced method for compression of medical images using wavelet transformation, normalization, and prediction. The compression method can be tuned to reproduce a good quality image close to the original image for the selected contextual area. Initially, the image undergoes 2D wavelet transform to obtain the approximate and the detailed coefficients. To ease the process of prediction, normalization is done for each sub-band separately, followed by mask-based prediction of the normalized coefficients. Finally, the prediction error coefficients are entropy-encoded using arithmetic coding technique. The proposed algorithm utilizes prediction as well as transformation to achieve a better compression along with good quality. The performance of the proposed system is compared with JPEG2000 and other conventional and contextual compression algorithms. The results show better performance quantitatively and visually. [ABSTRACT FROM PUBLISHER]

The JPEG2000 image compression standard is the powerful encoder which can provide phenomenal rate-control performance. The post-compression rate-distortion (PCRD) algorithm in JPEG2000 is not efficient. It requires encoding all coding passes even though a large contribution of them will not be contained in the final code-stream. Tier-1 encoding in the JPEG2000 standard takes a significant amount of memory and coding time. In this work, a low-complexity rate distortion method for JPEG2000 is proposed. It is relied on a reverse order for the resolution levels and the coding passes. The proposed algorithm encodes only the coding passes contained in the final code-stream and it does not need any post compression rate control part. The computational complexity of proposed algorithm is negligible, making it suitable to compression and attaining a significant performance. Simulations results show that the proposed algorithm obtained the PSNR values are comparable with the optimal PCRD. [ABSTRACT FROM AUTHOR]

This paper presents an efficient technique for satellite multispectral image compression aiming at reducing the size of storage of multispectral images with high-quality reconstruction. The proposed technique is based on removing sub-bands before compression. The removed sub-bands are determined using the correlation coefficients between bands. In the compression process, we use the Discrete Wavelet Transform (DWT) followed by an entropy coder (e.g., a Huffman or an arithmetic encoder) for the most correlated bands. Moreover, we use JPEG2000 to compress the rest of bands with Principal Component Analysis (PCA) as a spectral decorrelation transform. Enhanced Thematic Mapper plus (ETM+) satellite multispectral images are used for the validation of the proposed technique. Experiments results demonstrate that the proposed technique improves the average multispectral image quality by 3–11 dB. The experimental results verify the effectiveness of the proposed technique. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging is an ubiquitous technique in solar physics observations and the recent advances in solar instrumentation enabled us to acquire and record data at an unprecedented rate. The huge amount of data which will be archived in the upcoming solar observatories press us to compress the data in order to reduce the storage space and transfer times. The correlation present over all dimensions, spatial, temporal and spectral, of solar data-sets suggests the use of a 3D base wavelet decomposition, to achieve higher compression rates. In this work, we evaluate the performance of the recent JPEG2000 Part 10 standard, known as JP3D, for the lossless compression of several types of solar data-cubes. We explore the differences in: a) The compressibility of broad-band or narrow-band time-sequence; I or V Stokes profiles in spectropolarimetric data-sets; b) Compressing data in [x,y, λ] packages at different times or data in [x,y,t] packages of different wavelength; c) Compressing a single large data-cube or several smaller data-cubes; d) Compressing data which is under-sampled or super-sampled with respect to the diffraction cut-off. [ABSTRACT FROM AUTHOR]

We review the state of the art in DNA microarray image compression and provide original comparisons between standard and microarray-specific compression techniques that validate and expand previous work. First, we describe the most relevant approaches published in the literature and classify them according to the stage of the typical image compression process where each approach makes its contribution, and then we summarize the compression results reported for these microarray-specific image compression schemes. In a set of experiments conducted for this paper, we obtain new results for several popular image coding techniques that include the most recent coding standards. Prediction-based schemes CALIC and JPEG-LS are the best-performing standard compressors, but are improved upon by the best microarray-specific technique, Battiato’s CNN-based scheme.

Stiskanje z izgubami se vse pogosteje uporablja v daljinskem zaznavanju, čeprav njegov vpliv na rezultate obdelav posnetkov še ni v celoti raziskan. V članku so predstavljeni učinki stiskanja JPEG2000 z izgubami na klasifikacijo posnetkov zelo visoke ločljivosti WorldView-2. Za klasifikacijo stisnjenih posnetkov smo prvič uporabili objektni metodi k-najbližji sosed (k-nearest neighbor) in metodo podpornih vektorjev (support vector machine), ki smo ju tudi primerjali. Rezultati razkrivajo vpliv stiskanja na same posnetke, segmentacijo in končno klasifikacijo. Študija dokazuje, da v splošnem stiskanje z izgubo ne vpliva negativno na klasifikacijo posnetkov, še več, v nekaterih primerih ima klasifikacija stisnjenih posnetkov boljše rezultate kot klasifikacija izvirnih posnetkov. Natančnost klasifikacij z metodo podpornih vektorjev kaže na možnost stiskanja podob do razmerja 30 : 1 brez izgube natančnosti klasifikacije. Najboljši rezultat z metodo k-najbližji sosed smo pridobili z najvišjim kompresijskim razmerjem (100 : 1). V raziskavi smo ugotovili, da metoda podpornih vektorjev daje boljše rezultate klasifikacije kot metoda k-najbližji sosed ter se priporoča tudi za nadaljnje raziskave. Poleg metode klasifikacije ima pri natančnosti rezultatov pomembno vlogo tudi segmentacija posnetkov ; Lossy compression is becoming increasingly used in remote sensing, although its effect on the processing results has yet not been fully investigated. This paper presents the effects of JPEG2000 lossy compression on the classification of very high-resolution WorldView-2 imagery. For the first time, the k-nearest neighbor and support vector machine methods of the object based classification were used. The results explore the impact of compression on the images, segmentation and resulting classification. The study proves that in general lossy compression does not adversely affect the classification of images; moreover, in some cases classification of compressed images yields better results than classification of the original image. Classification accuracy of the support vector machine method indicates that compression ratios of up to 30:1 can be used without any loss of classification accuracy. The best result of the k-nearest neighbor method was obtained with the highest compression ratio (100:1). The support vector machine is recommended for further research. In addition to the classification method, image segmentation also plays an important role in the accuracy of the results.

Medical imaging techniques produce prohibitive amounts of digitized clinical data. Compression of medical images is a must due to large memory space required for transmission and storage. This paper presents an effective algorithm to compress and to reconstruct medical images. The proposed algorithm first extracts edge information of medical images by using fuzzy edge detector. The images are decomposed using Cohen-Daubechies-Feauveau (CDF) wavelet. The hybrid technique utilizes the efficient wavelet based compression algorithms such as JPEG2000 and Set Partitioning In Hierarchical Trees (SPIHT). The wavelet coefficients in the approximation sub band are encoded using tier 1 part of JPEG2000. The wavelet coefficients in the detailed sub bands are encoded using SPIHT. Consistent quality images are produced by this method at a lower bit rate compared to other standard compression algorithms. Two main approaches to assess image quality are objective testing and subjective testing. The image quality is evaluated by objective quality measures. Objective measures correlate well with the perceived image quality for the proposed compression algorithm.

Compression of hyperspectral imagery increases the efficiency of image storage and transmission. It is especially useful to alleviate congestion in the downlinks of planes and satellites, where these images are usually taken from. A novel compression algorithm is presented here. It first spectrally decorrelates the image using Vector Quantization and Principal Component Analysis (PCA), and then applies JPEG2000 to the Principal Components (PCs) exploiting spatial correlations for compression. We take advantage of the fact that dimensionality reduction preserves more information in the first components, allocating more depth to the first PCs. We optimize the selection of parameters by maximizing the distortion-ratio performance across the test images. An increase of 1 to 3 dB in Signal Noise Ratio (SNR) for the same compression ratio is found over just using PCA + JPEG2000, while also speeding up compression and decompression by more than 10%. A formula is proposed which determines the configuration of the algorithm, obtaining results that range from heavily compressed-low SNR images to low compressed-near lossless ones.

Çoklu ortam teknolojilerindeki gelişmeler, görüntü işleme ve sıkıştırmanın da önemini artırmıştır. Kayıplı ve kayıpsız sıkıştırma yöntemleri kullanılarak görüntü kalitesini kabul edilemeyecek seviyeye düşürmeden boyutu küçültülen görüntüler bellekte daha az yer kaplarlar. Böylece internet veya mobil araçlar üzerinden daha kısa sürede gönderilip alınabilirler. Dalgacık tabanlı bir görüntü sıkıştırma standardı olan JPEG2000, Joint Photographic Expert Group (JPEG) komitesi tarafından daha önceki JPEG standardının yerini alması amacı ile oluşturulmuştur. Bazı ek parçalarının geliştirilmesi halen sürmektedir. Bu çalışmada Visual C# 2005 ile, kenar belirleme ve gürültü azaltma gibi önemli görüntü işleme tekniklerini destekleyen bir yazılım aracı geliştirilmiştir. Uygulamanın önemli bir özelliği, JPEG2000 standardı da dahil olmak üzere birçok görüntü biçimini desteklemekte ve sadece iki boyutlu resimler değil aynı zamanda çok boyutlu görüntüler üzerinde de işlemler yapılabilmesine olanak sağlamaktadır. Aynı zamanda, modern yazılım geliştirme platformlarının görüntü işleme destekleri de karşılaştırılmış ve geliştirilen yazılımın çeşitli özellikleri belirtilmiştir.

Çoklu ortam teknolojilerindeki gelişmeler, görüntü işleme ve sıkıştırmanın da önemini artırmıştır. Kayıplı ve kayıpsız sıkıştırma yöntemleri kullanılarak görüntü kalitesini kabul edilemeyecek seviyeye düşürmeden boyutu küçültülen görüntüler bellekte daha az yer kaplarlar. Böylece internet veya mobil araçlar üzerinden daha kısa sürede gönderilip alınabilirler. Dalgacık tabanlı bir görüntü sıkıştırma standardı olan JPEG2000, Joint Photographic Expert Group (JPEG) komitesi tarafından daha önceki JPEG standardının yerini alması amacı ile oluşturulmuştur. Bazı ek parçalarının geliştirilmesi halen sürmektedir. Bu çalışmada Visual C# 2005 ile, kenar belirleme ve gürültü azaltma gibi önemli görüntü işleme tekniklerini destekleyen bir yazılım aracı geliştirilmiştir. Uygulamanın önemli bir özelliği, JPEG2000 standardı da dahil olmak üzere birçok görüntü biçimini desteklemekte ve sadece iki boyutlu resimler değil aynı zamanda çok boyutlu görüntüler üzerinde de işlemler yapılabilmesine olanak sağlamaktadır. Aynı zamanda, modern yazılım geliştirme platformlarının görüntü işleme destekleri de karşılaştırılmış ve geliştirilen yazılımın çeşitli özellikleri belirtilmiştir.

Scalable coding-based content adaptation poses serious challenges to traditional watermarking algorithms, which do not consider the scalable coding structure and hence cannot guarantee correct watermark extraction in media consumption chain. In this paper, we propose a novel concept of scalable blind watermarking that ensures more robust watermark extraction at various compression ratios while not effecting the visual quality of host media. The proposed algorithm generates scalable and robust watermarked image code-stream that allows the user to constrain embedding distortion for target content adaptations. The watermarked image code-stream consists of hierarchically nested joint distortion-robustness coding atoms. The code-stream is generated by proposing a new wavelet domain blind watermarking algorithm guided by a quantization based binary tree. The code-stream can be truncated at any distortion-robustness atom to generate the watermarked image with the desired distortion-robustness requirements. A blind extractor is capable of extracting watermark data from the watermarked images. The algorithm is further extended to incorporate a bit-plane discarding-based quantization model used in scalable coding-based content adaptation, e.g., JPEG2000. This improves the robustness against quality scalability of JPEG2000 compression. The simulation results verify the feasibility of the proposed concept, its applications, and its improved robustness against quality scalable content adaptation. Our proposed algorithm also outperforms existing methods showing 35% improvement. In terms of robustness to quality scalable video content adaptation using Motion JPEG2000 and wavelet-based scalable video coding, the proposed method shows major improvement for video watermarking. [ABSTRACT FROM PUBLISHER]

Medical technology has undergone major breakthroughs in recent years, especially in the area of the examination tools for diagnostic purposes. The traditional examination method for the diagnosis of gastrointestinal diseases is gastroscopy with the use of an endoscope. Wireless camera pill has created a new perspective for engineers and physicians. After years of great innovation, commercial swallowable pills have been produced and applied in clinical practice. These pills can cover the examination of the gastrointestinal system and provide to the physicians not only a lot more useful data that is not available from the traditional methods, but also elimination of the use of the painful endoscopy procedure. In this paper, a new design of the wireless swallowable pills has been proposed. It takes advantage of the benefits of every sub-system, like camera lenses, image compressor and RF sub-system. In this way our system can provide enough and accurate data to the physicians. [ABSTRACT FROM AUTHOR]

Despite all the technological developments in image acquisition and processing, preserving old documents and other data of historical interest is still a very challenging issue. Indeed, these documents are often proned to several types of artifacts affecting their readability. Furthermore, due to the considerable information considered in such media, reducing the size of the digitized documents is another challenging problem since their entropy is often high due to the presence of artifacts. Thus, we believe that directing the lost of information onto artifacts could bring an elegant solution to this issue. In this paper, we propose the first approach joining enhancement-compression for handwritten document images. This approach presents a novel foreground/background segmentation algorithm, using both directional and contrast features to highlight the original information. This pre-treatment step is embedded into DjVu encoder, which is commonly used in National Archives and libraries frameworks, to drive the compression rate. Both objective evaluation and perceptual judgment demonstrate the efficiency of the proposed scheme on the whole DIBCO datasets. [ABSTRACT FROM AUTHOR]

This paper proposes an effectual sample entropy (SampEn) based complexity sorting pre-processing technique for two dimensional electrocardiogram (ECG) data compression. The novelty of the approach lies in its ability to compress the quasi-periodic ECG signal by exploiting the intra and inter-beat correlations. The proposed method comprises the following steps: (1) QRS detection, (2) Length normalization, (3) Dc equalization, (4) SampEn based nonlinear complexity sorting and (5) Compression using JPEG2000 Codec. The performance has been evaluated over 48 records from the MIT-BIH arrhythmia database. The average quality score (QS) measurements at different residual errors were 42.25, 4.73, and 2.75 for percentage root mean square difference (PRD), PRD1024, and PRD Normalized respectively. The work also reports extensive experimentations on the compressor for various durations of the ECG records (5–30 min, with 5-min increment). The proposed algorithm demonstrates significantly better performance in comparison to the contemporary state-of-the-art works present in the literature. [ABSTRACT FROM AUTHOR]