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Network development has made it easier to access multimedia material and to change it by allowing the collection, modification, and transmission of digital data. Additionally, this has led to a rise in malicious use, including unauthorized data distribution and copying. As the quantity of evil activities increases, security issues such as unauthorized use and image forgery are rising. While security solutions for JPEG-1 images are widely available, there remains a significant gap in protection for JPEG 2000 images. In this paper, we propose a new watermark-based forgery detection method to comply with the JPEG Privacy and Security standards and to authenticate JPEG 2000 image integrity in the discrete wavelet transform (DWT) domain. The method proposed divides JPEG 2000 images into groups of blocks (GOBs) within the DWT domain. The watermark is generated by collaborating with the neighboring GOBs and is embedded in the GOBs. It enables you to respond to the collage attack. Experimental results using various sample images show the superiority of the proposed method, showing negligible visual differences between the original and watermarked JPEG 2000 images. [ABSTRACT FROM AUTHOR]

The main aim of this research work is to compress grayscale images efficiently using prediction and intensity-based image compression algorithms. Image compression is useful for removing the duplication in an image to store and transmit the data in an efficient form. This research work analyzes four new schemes for gray scale lossy image compression. Among the four schemes considered, two compressive approaches are designed for Prediction Based Image Compression (PBIC) level implementation. Third approach is designed for Intensity Based Image Compression (IBIC). Finally, the previously designed PBIC and IBIC schemes lead to an Integrated Encoder. All the considered method performances are analyzed using the performance metrics. These results are compared with JPEG 2000 which is a extensively used benchmark compression encoder. The outcome of all the proposed methods is also compared with modern encoders. [ABSTRACT FROM AUTHOR]

Network development has made it easier to access multimedia material and to change it by allowing the collection, modification, and transmission of digital data. Additionally, this has led to a rise in malicious use, including unauthorized data distribution and copying. As the quantity of evil activities increases, security issues such as unauthorized use and image forgery are rising. While security solutions for JPEG-1 images are widely available, there remains a significant gap in protection for JPEG 2000 images. In this paper, we propose a new watermark-based forgery detection method to comply with the JPEG Privacy and Security standards and to authenticate JPEG 2000 image integrity in the discrete wavelet transform (DWT) domain. The method proposed divides JPEG 2000 images into groups of blocks (GOBs) within the DWT domain. The watermark is generated by collaborating with the neighboring GOBs and is embedded in the GOBs. It enables you to respond to the collage attack. Experimental results using various sample images show the superiority of the proposed method, showing negligible visual differences between the original and watermarked JPEG 2000 images.

For any digital application with document images such as retrieval, the classification of document images becomes an essential stage. Conventionally for the purpose, the full versions of the documents, that is the uncompressed document images make the input dataset, which poses a threat due to the big volume required to accommodate the full versions of the documents. Therefore, it would be novel, if the same classification task could be accomplished directly (with some partial decompression) with the compressed representation of documents in order to make the whole process computationally more efficient. In this research work, a novel deep learning model—DWT-CompCNN—is proposed for classification of documents that are compressed using High Throughput JPEG 2000 (HTJ2K) algorithm. The proposed DWT-CompCNN comprises of five convolutional layers with filter sizes of 16, 32, 64, 128, and 256 consecutively for each increasing layer to improve learning from the wavelet coefficients extracted from the compressed images. Experiments are performed on two benchmark datasets, Tobacco-3482 and RVL-CDIP, which demonstrate that the proposed model is time and space efficient, and also achieves a better classification accuracy in compressed domain. [ABSTRACT FROM AUTHOR]

Lossless compression is still a challenging task in the case of microarray images. This research proposes two algorithms that aim to improve the lossless compression efficiency for high spatial resolution microarray images using general entropy codecs, namely Huffman and arithmetic coders and the image compression standard JPEG 2000. Using the standards ensures that decoders are available to reassess the images for future applications. Typically, microarray images have a bit-depth of 16. In proposed algorithm 1, every image's per bit-plane entropy profile is calculated to automatically determine a better threshold T to split the bit-planes into the foreground and background sub-images. T is initially set to 8. However, in algorithm 1, T is updated, balancing the average value of per bit-plane entropies of the segmented sub-images of an image for improved lossless compression results. Codecs are applied individually to the produced sub-images. Proposed algorithm 2 is designed to increase the lossless compression efficiency of any unmodified JPEG 2000-compliant encoder while reducing side information overhead. In this, pixel intensity reindexing and, thereby, changing the histograms of the same segmented sub-images obtained from algorithm 1 are implemented and confirmed to get better JPEG 2000 results in lossless mode than applying it to the original image. The lossless JPEG 2000 compression performance on microarray images is also compared to JPEG-LS in particular. The experiments are carried out to validate the methods on seven benchmark datasets, namely ApoA1, ISREC, Stanford, MicroZip, GEO, Arizona, and IBB. The average first-order entropy of the datasets above is calculated and compared for codecs and better than competitive efforts in the literature. [ABSTRACT FROM AUTHOR]

JPEG 2000 is a popular image compression technique that uses Discrete Wavelet Transform (DWT) for compression and subsequently provides many rich features for efficient storage and decompression. Though compressed images are preferred for archival and communication purposes, their processing becomes difficult due to the overhead of decompression and re-compression operations which are needed as many times the data needs to operate. Therefore in this research paper, the novel idea of direct operation over the JPEG 2000 compressed documents is proposed for extracting text and non-text regions without using any segmentation algorithm. The technique avoids full decompression of the compressed document in contrast to the conventional methods, where they fully decompress and then process. Moreover, JPEG 2000 features are explored in this research work to partially and intelligently decompress only the selected regions of interest at different resolutions and bitdepths to accomplish segmentation-less extraction of text and non-text regions. Finally Maximally Stable Extremal Regions (MSER) algorithm is used to extract the layout of segmented text and non-text regions for further analysis. Experiments have been carried out on the standard PRImA Layout Analysis Dataset leading to promising results and saving computational resources.

In this paper we propose novel extensions to JPEG 2000 for the coding of discontinuous media which includes piecewise smooth imagery such as depth maps and optical flows. These extensions use breakpoints to model discontinuity boundary geometry and apply a breakpoint dependent Discrete Wavelet Transform (BP-DWT) to the input imagery. The highly scalable and accessible coding features provided by the JPEG 2000 compression framework are preserved by our proposed extensions, with the breakpoint and transform components encoded as independent bit streams that can be progressively decoded. Comparative rate-distortion results are provided along with corresponding visual examples which highlight the advantages of using breakpoint representations with accompanying BD-DWT and embedded bit-plane coding. Recently our proposed extensions have been adopted and are in the process of being published as a new Part 17 to the JPEG 2000 family of coding standards. [ABSTRACT FROM AUTHOR]

Advanced therapeutic imaging innovation produces an immense amount of information, predominantly from processed tomography and other imaging modalities. This causes a significant challenge when storing them on a local personal computer or communicating them over cyberspace. Therefore, a proficient image compression system is fundamentally required. From this perspective, this paper proposes a lossless image compression procedure by reducing image dimension and using a prediction technique. In the proposed strategy, the column dimension of a grey-scale image is first reduced and then the prediction errors are encoded using Huffman coding. The decoding process is carried out in the reverse direction. The proposed method is executed and applied to several bench-marked images. The performance of this proposed algorithm is assessed and compared with the state-of-the-art techniques based on several assessment criteria, such as average code length (ACL), compression ratio (CR), encoding time, decoding time, efficiency, peak signal to noise ratio (PSNR) and normalised correlation (NC). The proposed algorithm also demonstrates an improvement in the average code length compared with the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Compression is known as reducing the memory size of an image. In the process of compression, there will be a loss of data then this type of compression is called lossy compression. If there is no loss of data, then this type of compression is called lossless compression. This new fingerprint compression algorithm is based on Singular Value Decomposition in sparse representation. In the algorithm, firstly the uploaded image is divided into patches then secondly dictionary is created for each fingerprint image patch. These patches are compressed using SVD in sparse representation. Then the image is reconstructed. This is a Lossless compression. The experiment demonstrates that our algorithm is efficient compared with several competing compression techniques (JPEG, JPEG 2000, and WSQ), especially at high compression ratios. Conclusions: Sparse representation is used for compression. JPEG has bad performance at low-bi rates and this is the more sophisticated algorithm. It provides a high Peak Signal to Noise Ratio and a high compression ratio. In this paper, a Fingerprint image is compressed using Singular Value Decomposition(SVD) in Sparse representation. The uploaded image occupies more storage size and requires more bandwidth and time to transfer. In this algorithm, Upload a fingerprint image from system memory into the application. This image is divided into patches. Dictionary is created for each patch of the image. SVD is used for the compression of the image. Reconstruction of the compressed image takes place. The resultant image after compression will have less storage size with the same quality. Bandwidth and time taken for the image transfer are less now. A graph is represented comparing the storage size of the uploaded image and the compressed image. [ABSTRACT FROM AUTHOR]

Legal matters, such as the investigation of a crime, rely heavily on fingerprint analysis. A fingerprint image, on the other hand, contains a staggering quantity of information. Because of this, we must limit the amount of data it contains. We'll need a powerful image compression method to do this. There are a plethora of methods for compressing images. [1] Images of finger prints are not always perfect quality. It's possible that skin and impression situations will damage or corrupt them. Thus, image enhancements prior to the extraction of minutiae, [5] various procedures are used to ensure a more accurate estimation of their placements. In this article, we'll look at a few different ways to compress fingerprints. This section concludes with an approach for compressing fingerprints. Sparsely populated. Our next step is to build a database of preconfigured fingerprint image patches. [3] Divide a fingerprint into small chunks called patches for a specific fingerprint. In order to obtain the, utilise the sparse representation approach the next step is to quantize the coefficients, and the final step is to encode them. Fingerprint images from three groups are tested in this experiment. The results of the tests show that our approach outperforms the competition in terms of compression efficiency. [7] The primary the minutiae is a feature that is used to compare two fingerprint images. As a result, the pre/post difference in minutiae There is some thought given to compression. [ABSTRACT FROM AUTHOR]

This paper describes in detail analysis of Digital Cinema Package (DCP) file format structure, which is today widely used for distribution of digital movies for digital cinema. The whole process from creating source files, following by intermediate steps, to the final form of this format, is presented as well. Security principles are covered by Key Delivery Message (KDM). Some free software for creation DCP are mentioned at the end of this paper.

Digital holography processing is a research topic related to the development of novel visual immersive applications. The huge amount of information conveyed by a digital hologram and the different properties of holographic data with respect to conventional photographic data require a comprehension of the performances and limitations of current image and video standard techniques. This paper proposes an architecture for objective evaluation of the performances of the state-of-the-art compression techniques applied to digital holographic data.

In this paper, improved Joint Source Channel (JSC) decoding using turbo codes is employed that are unified into JPEG 2000 decoder architecture. Rather than using conventional decoding approach, the proposed system provides reduced BER and complexity. This work deliberates Soft Output Viterbi Algorithm (SOVA) for its real time implementation features. Nevertheless, the algorithm suffers from performance degradation due to optimistic and correlation effects. The objective of this work is to enhance the performance of SOVA by using appropriate reduction factors that integrates and eliminates both these distortions. Acquired Integrated Factor is incorporated in the turbo decoders of the image transmission system. Bit Error rate and Peak Signal to Noise Ratio (PSNR) analysis is made by considering Additive White Gaussian Noise and Rayleigh fading channel through MATLAB simulation. To illustrate the visual quality improvement of the proposed scheme, comparison with other FEC codes is done with significant PSNR gains. [ABSTRACT FROM AUTHOR]

Most multimedia source encoders produce scalable bit streams that are vulnerable to noisy channel effects. Unequal error protection (UEP) designs are attractive solutions for such scalable bit streams. In the other hand, the structure of multiple concatenated codes, which consist of the combination of two or more elementary encoders and interleavers, offers a flexible implementation of unequal data protection. To provide an efficient protection for embedded source bit streams, an adaptive concatenated coding/decoding design is proposed in this paper inspired by serially concatenated turbo codes. This scheme involves iterative decoding of concatenated codes for JPEG 2000 image transmission. In the case of time varying channels, the concatenated coding rates are adaptively chosen to maximize the visual quality. The simulation results show that the proposed scheme achieves better image quality improvements at the receiver side compared with a conventional multiple turbo codes-based design, with significant peak-signal-to-noise ratio gains.

In this paper an unequal error protection scheme based on Rate-Compatible Punctured Convolutional codes employing Hierarchical PSK modulation is presented. New error bounds are derived and reported for AWGN and flat Rayleigh fading channels. Both soft and hard decision decoding techniques are considered in our analysis. The proposed scheme does not involve increased in complexity of resources and bandwidth expansion either. It is shown that by varying the signal to noise ratio and a specific constellation parameter set, which controls the level of error protection, significant error performance can be achieved over the uniform modulation case. All the established theoretical upper bounds are illustrated with numerical examples and these are also simulated using Matlab Simulink® 2017. Simulation results show that near-13-dB difference is achieved between the first and last priority sub-channels of the UEP communication system. The proposed scheme can find suitable applications in digital video broadcasting and Wireless JPEG2000 as well as H.265 data transmission systems. [ABSTRACT FROM AUTHOR]

Image segmentation lies at the heart of multiple image processing chains, and achieving accurate segmentation is of utmost importance as it affects later processing. Image segmentation has recently gained interest in the field of remote sensing, mostly due to the widespread availability of remote sensing data. This increased availability poses the problem of transmitting and storing large volumes of data. Compression is a common strategy to alleviate this problem. However, lossy or near-lossless compression prevents a perfect reconstruction of the recovered data. This letter investigates the image segmentation performance in data reconstructed after a near-lossless or a lossy compression. Two image segmentation algorithms and two compression standards are evaluated on data from several instruments. Experimental results reveal that segmentation performance over previously near-lossless and lossy compressed images is not markedly reduced at low and moderate compression ratios (CRs). In some scenarios, accurate segmentation performance can be achieved even for high CRs. [ABSTRACT FROM AUTHOR]

The Block Decoder (BD) which is an indispensable component of the JPEG 2000 image compression standard has the highest computational complexity and determines the speed of the overall decoder system. This paper proposes a high throughput pass parallel BD architecture, which can decode more than one bit per clock cycle. In BD, the dependency between context generation and arithmetic decoding unit incorporates stalling and reduces the throughput of the decoding process. The proposed selective byte input and synchronous sample skipping techniques are used to prevent stalling in the decoding process. The proposed architecture achieves 86% more throughput with 50% increment in the hardware cost than that of the best available serial BD architecture. In comparison with the best available pass parallel architecture, throughput improves almost 8.2 times with 61% increment in the hardware cost. Incorporation of the speed up techniques in the design is the main reason for more hardware consumption. The Figure of Merit of the proposed design, which is the ratio of throughput and hardware cost, is more than that of the available BD architectures for typical code block (CB) size of 32 × 32. The ASIC implementation of the proposed design consumes 66 mW power at maximum operating frequency. • High throughput pass parallel Block Decoder architecture for Point-of-Care applications. • Synchronous sample skipping and selective byte import techniques are proposed to improve throughput. • Efficient 3-subbank memory management technique is proposed to reduce unnecessary state memory access. [ABSTRACT FROM AUTHOR]

Abstract Current image coding standards provide limited support for privacy and security features. An exception is the JPSEC standard, which defines security extensions in JPEG 2000 specifications (part 8). Notwithstanding this shortcoming, the JPEG committee is currently defining a new JPEG Systems standard, which envisages privacy and security support across JPEG family of standards. In this manuscript, the main philosophy of this emerging specification is outlined along with typical use cases, main requirements as well as examples of potential technological solutions. The upcoming specification guarantees backward and forward compatibility with earlier standards and legacy implementations. Finally, we illustrate the introduced framework by two applications targeting secure photo sharing on social networks and IPR management in the GLAM sector.

This paper describes in details analysis of Digital Cinema Package (DCP) file format structure, which is today widely used for distribution of digital movies for digital cinema. The whole process from creating source files, following by intermediate steps, to the final form of this format, is presented as well. Security principles are covered by Key Delivery Message (KDM). Some free software for creation DCP are mentioned at the end of this paper. [ABSTRACT FROM AUTHOR]

We implemented a more efficient circuit for one-dimensional (1-D) forward discrete wavelet transform (DWT) 5/3 filter. Our design utilizes processing and memory resources that are wasted in some other state-of-the-art solutions and is at least 33% simpler in terms of used registers, is 17% simpler in terms of used logic elements, has 7% higher maximum operating frequency and has 2% lower total power dissipation than previously published designs. The advantages of our design are achieved by a novel non-stationary filter topology which reuses the same registers for generating both low-pass and high-pass output coefficients, in different time slots, due to feed-forward and feedback paths. Our design is suitable for image compression systems which use 5/3 filter, e.g., JPEG 2000. We also proposed two-dimensional (2-D) DWT 5/3 architecture which uses implemented 1-D DWT filter design. The proposed 2-D DWT architecture outperforms all previously published architectures in terms of required memory capacity, which is at least 20% lower than memory capacity in any other reported solution. [ABSTRACT FROM AUTHOR]

With the augmentation in multimedia technology, demand for high-speed real-time image compression system has also increased. JPEG 2000 is the latest generation of static image coding algorithm which builds and improves on its predecessor JPEG. In JPEG 2000, the embedded block coding with optimal truncation is the core of entropy coding algorithm and the most important element to calculate the very hard portion in the compressing process of JPEG 2000 image compression standard. Various applications, such as medical imaging, satellite imagery, digital cinema, and others, require high-speed, high-performance BPC architecture. Hardware requirement of these existing architectures is very high, and throughput is low. To solve this problem, an efficient bit plane coder (BPC) architecture has been proposed in which three coding passes operate in parallel and are allowed to progress independently to scan the four bits of each column in parallel. The entire design of BPC encoder is tested on the field-programmable gate array platform. The implementation results show on the one hand that throughput of the proposed architecture of BPC architecture operates at 434.59 MHz. On the other hand, our design outperforms well-known techniques with respect to the processing time, such that it is capable of encoding one bit plane in 232 clock cycles under any circumstances. It can reach 90% reduction when compared to bit plane sequential. Moreover, it is capable of encoding digital cinema size (2048 × 1080) at 42 frames per second. Therefore, it satisfies the requirement of applications like cartography, medical imaging, satellite imagery, and others, which demand high-speed real-time image compression system. [ABSTRACT FROM AUTHOR]

Among the variety of multimedia formats, color images play a prominent role. A technique for lossless compression of color images is introduced. The technique is composed of first transforming a red, green, and blue image into luminance and chrominance domain (YC[sub u]C[sub v]). Then, the luminance channel Y is compressed with a context-based, adaptive, lossless image coding technique (CALIC). After processing the chrominance channels with a hierarchical prediction technique that was introduced earlier, Burrows-Wheeler inversion coder or JPEG 2000 is used in compression of those C[sub u] and C[sub v] channels. It is demonstrated that, on a wide variety of images, particularly on medical images, the technique achieves substantial compression gains over other well-known compression schemes, such as CALIC, M-CALIC, Better Portable Graphics, JPEG-LS, JPEG 2000, and the previously proposed hierarchical prediction and context-adaptive coding technique LCIC. [ABSTRACT FROM AUTHOR]

Remote sensing applications have gained in popularity in recent years, which has resulted in vast amounts of data being produced on a daily basis. Managing and delivering large sets of data becomes extremely difficult and resource demanding for the data vendors, but even more for individual users and third party stakeholders. Hence, research in the field of efficient remote sensing data handling and manipulation has become a very active research topic (from both storage and communication perspectives). Driven by the rapid growth in the volume of optical satellite measurements, in this work we explore the lossy compression technique for multispectral satellite images. We give a comprehensive analysis of the High Efficiency Video Coding (HEVC) still-image intra coding part applied to the multispectral image data. Thereafter, we analyze the impact of the distortions introduced by the HEVC’s intra compression in the general case, as well as in the specific context of crop classification application. Results show that HEVC’s intra coding achieves better trade-off between compression gain and image quality, as compared to standard JPEG 2000 solution. On the other hand, this also reflects in the better performance of the designed pixel-based classifier in the analyzed crop classification task. We show that HEVC can obtain up to 150:1 compression ratio, when observing compression in the context of specific application, without significantly losing on classification performance compared to classifier trained and applied on raw data. In comparison, in order to maintain the same performance, JPEG 2000 allows compression ratio up to 70:1.