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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]

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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]