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With the emergence of light field imaging in recent years, the compression of its elementary image array (EIA) has become a significant problem. Our coding framework includes modeling and reconstruction. For the modeling, the covariance-matrix form of the 4-D Epanechnikov kernel (4-D EK) and its correlated statistics were deduced to obtain the 4-D Epanechnikov mixture models (4-D EMMs). A 4-D Epanechnikov mixture regression (4-D EMR) was proposed based on this 4-D EK, and a 4-D adaptive model selection (4-D AMLS) algorithm was designed to realize the optimal modeling for a pseudo video sequence (PVS) of the extracted key-EIA. A linear function based reconstruction (LFBR) was proposed based on the correlation between adjacent elementary images (EIs). The decoded images realized a clear outline reconstruction and superior coding efficiency compared to high-efficiency video coding (HEVC) and JPEG 2000 below approximately 0.05 bpp. This work realized an unprecedented theoretical application by (1) proposing the 4-D Epanechnikov kernel theory, (2) exploiting the 4-D Epanechnikov mixture regression and its application in the modeling of the pseudo video sequence of light field images, (3) using 4-D adaptive model selection for the optimal number of models, and (4) employing a linear function-based reconstruction according to the content similarity.

Factorized in the lifting structure, the wavelet transform can easily be extended by arbitrary compensation methods. Thereby, the transform can be adapted to displacements in the signal without losing the ability of perfect reconstruction. This leads to an improvement of scalability. In temporal direction of dynamic medical 3-D+t volumes from Computed Tomography, displacement is mainly given by expansion and compression of tissue. We show that these smooth movements can be well compensated with a mesh-based method. We compare the properties of triangle and quadrilateral meshes. We also show that with a mesh-based compensation approach coding results are comparable to the common slice wise coding with JPEG 2000 while a scalable representation in temporal direction can be achieved.

Recently, more interest in the different plenoptic formats, including digital holograms, has emerged. Aside from other challenges that several steps of the holographic pipeline, from digital acquisition to display, have to face, visual quality assessment of compressed holograms is particularly demanding due to the distinct nature of this 3D image modality when compared to regular 2D imaging. There are few studies on holographic data quality assessment, particularly with respect to the perceptual effects of lossy compression. This work aims to study the quality evaluation of digital hologram reconstructions presented on regular 2D displays in the presence of compression distortions. As there is no established or generally agreed on compression methodology for digital hologram compression on the hologram plane with available implementations, a set of state-of-the-art compression codecs, namely, HEVC, AV1, and JPEG2000, were used for compression of the digital holograms on the object plane. Both computer-generated and optically generated holograms were considered. Two subjective tests were conducted to evaluate distortions caused by compression. The first subjective test was conducted on the reconstructed amplitude images of central views, while the second test was conducted on pseudovideos generated from the reconstructed amplitudes of different views. The subjective quality assessment was based on mean opinion scores. A selection of objective quality metrics was evaluated, and their correlations with mean opinion scores were computed. The VIFp metrics appeared to have the highest correlation.

Given the uniqueness of synthetic aperture radar (SAR) images, traditional optical image compression algorithms cannot fully exploit their redundant information. To improve SAR image compression in terms of rate-distortion performance and visual perception, an end-to-end SAR image compression convolutional neural network (CNN) model based on a variational autoencoder is proposed. The proposed CNN model consists of a main autoencoder and a hyper autoencoder. To reduce dependencies in latent space, a joint transform of linear CNN and nonlinear generalized divisive normalization (GDN) activation is applied in the main autoencoder. Moreover, residual blocks are combined with the transforms to boost the efficiency of feature learning and make use of subpixels to improve the quality of reconstructed images. Instead of a fixed entropy model, a conditioned entropy model that works with a hyperprior network is used to learn the distribution of latents, which helps to further improve the compression quality. During training, the model is optimized by evaluating the rate-distortion performance. The experimental results show that the proposed method can achieve better distortion performance than JPEG, JPEG2000, and the available CNN-based method in terms of objective evaluation criteria and human vision perception quality.

Chinese remainder theorem (CRT) is widely utilized in many cryptographic applications and additionally the reversible nature of CRT is employed in compression of images. This paper mainly focuses on the suitability of CRT for lossless image compression and the analysis is carried out for the number and range of primes to be chosen. With respect to the analysis is carried out for the number of primes to be chosen (i.e., 2, 3, 4, 5, and 6), it is found that CRT suits well only for the chosen number of primes 2 with good compression ratio. For the remaining prime numbers, it provides negligible or even negative CR based on the chosen number of prime numbers. Also, CRT based lossless compression (CRTLC) reduces the size of the image based on the number of primes chosen. Further, it can achieve substantial compression of the original image. Using different test images, CRT is compared with recent lossless compression methods and against the standard set of lossless compression techniques (i.e., JPEG 2000, JPEG-LS, and CALIC). From these comparisons, it is inferred that CRT scores (maximum achieved CR is 1.8823) better than the recent and standard algorithms.

Lossy image compression is likely to produce blurred images which leads to erroneous image-level understanding. The human visual system (HVS) is focused on the area of interest present in the image. Motivated by this fact, we propose a compression-decompression algorithm focusing on the important content present in different parts of the image. Using the concept of the Guided Grad-CAM (Gradient-weighted Class Activation Mapping) technique to produce heat maps with the help of a heat map generator trained on ResNet-50, a saliency-guided encoding–decoding algorithm is developed. A wider multi-scale saliency guided convolutional neural network (MsSG-CNN) is designed, in which the notion of convolution with different size filters helps to obtain unique but different features. The feature extraction followed by multilevel fusion of features helps deep neural network (DNN) to capture contextual information and obtain high quality, good resolution, visually pleasing images with fine details. The proposed algorithm is tested on the Kodak benchmark dataset, CLIC 2019 challenging dataset, and FDDB facial images dataset. At low bit rates, the MS-SSIM of the proposed algorithm is found to be superior to JPEG, JPEG2000, BPG, WebP, and Minnen’s approaches with approximately up to 60%, 24.80%, 11.43%, 23.08% & 75% gains respectively, which is quite a significant improvement, when tested on the Kodak dataset. Similarly, at high bit rates, the improvement in MS-SSIM is approximately up to 41.67%, 37.30%, 23.90% 34.21% & 13.33% when compared with JPEG, JPEG2000, BPG, WebP, and Minnen’s approaches respectively. The improvement in PSNR at low and high bit rates is approximately up to 11.32%, 5%, 5.26% and 5.6%, 10.29%, 8.7% as compared to JPEG, Balle’s, and Lee’s algorithms respectively. The PSNR-HVS has been improved by approximately up to 27.27%, 19.15%, and 28.33%, 28% as compared to JPEG and Toderici’s algorithms respectively at low and high bit-rates. A similar type of improvement is obtained with FDDB and CLIC 2019 datasets also, which is discussed in the paper.

The main characteristics of graphic information compression methods with losses and without losses (RLE, LZW, Huffman's method, DEFLATE, JBIG, JPEG, JPEG 2000, Lossless JPEG, fractal and Wawelet) are analyzed in the article. Effective transmission and storage of images in railway communication systems is an important task now. Because large images require large storage resources. This task has become very important in recent years, as the problems of information transmission by telecommunication channels of the transport infrastructure have become urgent. There is also a great need for video conferencing, where the task is to effectively compress video data - because the greater the amount of data, the greater the cost of transmitting information, respectively. Therefore, the use of image compression methods that reduce the file size is the solution to this task. The study highlights the advantages and disadvantages of compression methods. The comparative analysis the basic possibilities of compression methods of graphic information is carried out. The relevance lies in the efficient transfer and storage of graphical information, as big data requires large resources for storage. The practical significance lies in solving the problem of effectively reducing the data size by applying known compression methods.

Image/video compression and communication need to serve both human vision and machine vision. To address this need, we propose a scalable image compression solution. We assume that machine vision needs less information that is related to semantics, whereas human vision needs more information that is to reconstruct signal. We then propose semantics-to-signal scalable compression, where partial bitstream is decodeable for machine vision and the entire bitstream is decodeable for human vision. Our method is inspired by the scalable image coding standard, JPEG2000, and similarly adopts subband-wise representations. We first design a trainable and revertible transform based on the lifting structure, which converts an image into a pyramid of multiple subbands; the transform is trained to make the partial representations useful for multiple machine vision tasks. We then design an end-to-end optimized encoding/decoding network for compressing the multiple subbands, to jointly optimize compression ratio, semantic analysis accuracy, and signal reconstruction quality. We experiment with two datasets: CUB200-2011 and FGVC-Aircraft, taking coarse-to-fine image classification tasks as an example. Experimental results demonstrate that our proposed method achieves semantics-to-signal scalable compression, and outperforms JPEG2000 in compression efficiency. The proposed method sheds light on a generic approach for image/video coding for human and machines.

In disease diagnosis, medical image plays an important part. Its lossless compression is pretty critical, which directly determines the requirement of local storage space and communication bandwidth of remote medical systems, so as to help the diagnosis and treatment of patients. There are two extraordinary properties related to medical images: lossless and similarity. How to take advantage of these two properties to reduce the information needed to represent an image is the key point of compression. In this paper, we employ the big data mining to set up the image codebook. That is, to find the basic components of images. We propose a soft compression algorithm for multi-component medical images, which can exactly reflect the fundamental structure of images. A general representation framework for image compression is also put forward and the results indicate that our developed soft compression algorithm can outperform the popular benchmarks PNG and JPEG2000 in terms of compression ratio.

High-Throughput JPEG 2000 (HTJ2K) is a royalty-free image compression standard published in 2019 that enhances JPEG 2000 by replacing its slow block coder with a fast block coder. The resulting speedup (e.g., >30× for lossless coding) accelerates the encoding and decoding of images, which can have a great impact on users. A less obvious benefit of the speedup is the enabling of low-latency applications, which was not possible with the original JPEG 2000 standard. Examples of these new potential applications are live, high-quality, low-latency broadcast video contribution, remote production, and Internet Protocol (IP)-based production on-premises and on cloud. This paper examines various HTJ2K encoding parameters and their impact on quality, bitrate, latency, and multigeneration encode/decode cycle performance in the context of requirements of broadcast and IP-based applications. Configurations include a number of different wavelet filters, code-block sizes, and wavelet decomposition structures that are available with HTJ2K. The performance of low-latency HTJ2K is also compared to other low-latency wavelet codecs like VC-2, JPEG XS, and JPEG 2000 Part-1. The JPEG 2000 Part-1 comparisons use the full-frame broadcast profile as well as the ultra-low latency (ULL) configuration as per Video Services Forum (VSF) Technical Recommendation (TR)-01:2018. A tradeoff between latency and compression quality is discussed .

The emergent ecosystems of intelligent edge devices in diverse Internet-of-Things (IoT) applications, from automatic surveillance to precision agriculture, increasingly rely on recording and processing a variety of image data. Due to resource constraints, e.g., energy and communication bandwidth requirements, these applications require compressing the recorded images before transmission. For these applications, image compression commonly requires: 1) maintaining features for coarse-grain pattern recognition instead of the high-level details for human perception due to machine-to-machine communications; 2) high compression ratio that leads to improved energy and transmission efficiency; and 3) large dynamic range of compression and an easy tradeoff between compression factor and quality of reconstruction to accommodate a wide diversity of IoT applications as well as their time-varying energy/performance needs. To address these requirements, we propose, MAGIC, a novel machine learning (ML)-guided image compression framework that judiciously sacrifices the visual quality to achieve much higher compression when compared to traditional techniques, while maintaining accuracy for coarse-grained vision tasks. The central idea is to capture application-specific domain knowledge and efficiently utilize it in achieving high compression. We demonstrate that the MAGIC framework is configurable across a wide range of compression/quality and is capable of compressing beyond the standard quality factor limits of both JPEG 2000 and WebP. We perform experiments on representative IoT applications using two vision data sets and show $42.65\times $ compression at similar accuracy with respect to the source. We highlight low variance in compression rate across images using our technique as compared to JPEG 2000 and WebP.

The detection of poor quality images for reasons such as focus, lighting, compression, and encoding is of great importance in the field of computer vision. The ability to quickly and automatically classify an image as poor quality creates opportunities for a multitude of applications such as digital cameras, phones, self-driving cars, and web search technologies. In this paper an end-to-end approach using Convolutional Neural Networks (CNN) is presented to classify images into six categories of bad lighting, Gaussian blur, motion blur, JPEG 2000, white-noise, and high quality reference images. A new dataset of images was produced and used to train and validate the model. Finally, the application of the developed model was evaluated using images from the German Traffic Sign Recognition Benchmark. The results show that the trained CNN can detect and correctly classify images into the aforementioned categories with high accuracy and the model can be easily re-calibrated for other applications with only a small sample of training images.

In this paper, a wavelet-based multimodal compression method is proposed. The method jointly compresses a medical image and an ECG signal within a single codec, i.e., JPEG-2000 in an effective and simple way. The multimodal scheme operates in two main stages: the first stage, consists of the encoder and involves a mixing function, aiming at inserting the samples of the signal in the image according to a predefined insertion pattern in the wavelet domain. The second stage represented by a separation function, consists of the extraction process of the ECG signal from the image after performing the decoding stage. Both the cubic spline and the median edge detection (MED) predictor have been adopted to conduct the interpolation process for estimating image pixels. Intensive experiments have been conducted to evaluate the performance of the multimodal scheme using objective distortion criteria. Results show clear superiority of the proposed scheme over the conventional separate compression approach involving two codecs: JPEG-2000 for images and ECG SPIHT-1D as well as other competing multimodal compression schemes in terms of both PRD and SNR at the signal decompression stage while maintaining good image quality and exhibiting a reduced computational complexity. Improvements in terms of average PRD and SNR values are as significant as 0.7 and 6 dB at low bit rates and 0.06 and 2 dB at higher bit rates on a number of test ECG signals and medical images.

To address the problem of ever-growing scientific data sizes making data movement a major hindrance to analysis, we introduce a novel encoding for scalar fields: a unified tree of resolution and precision, specifically constructed so that valid cuts correspond to sensible approximations of the original field in the precision-resolution space. Furthermore, we introduce a highly flexible encoding of such trees that forms a parameterized family of data hierarchies. We discuss how different parameter choices lead to different trade-offs in practice, and show how specific choices result in known data representation schemes such as zfp [52] , idx [58] , and jpeg 2000 [76] . Finally, we provide system-level details and empirical evidence on how such hierarchies facilitate common approximate queries with minimal data movement and time, using real-world data sets ranging from a few gigabytes to nearly a terabyte in size. Experiments suggest that our new strategy of combining reductions in resolution and precision is competitive with state-of-the-art compression techniques with respect to data quality, while being significantly more flexible and orders of magnitude faster, and requiring significantly reduced resources.

Advanced image scanning techniques produce high resolution medical images such as CT, MRI which in turn needs large storage space and bandwidth for transmitting over a network. Lossless compression is preferred for medical images to preserve important diagnostic details. However, it is only sufficient to maintain the high quality of an image in a diagnostically important region, namely Region of interest (ROI) for an accurate diagnosis. Non -ROI portion when compressed near-losslessly does not affect the image quality but reduces the file size effectively. We propose a compression technique where the prediction is done by Resolution Independent Gradient Edge Detector (RIGED) to de-correlate the image pixels and block-based arithmetic coding is used for encoding. The optimal threshold value, optimal q-level and the block-based coding removes inter-pixel, psycho-visual and coding redundancy from non-ROI part to achieve high compression whereas ROI part is compressed losslessly by removing inter-pixel and coding redundancy only. In this paper, optimal threshold-based predictive lossless compression in the ROI and optimal quantization (q) based near-lossless compression in the rest of the region is proposed. The proposed method is evaluated on volumetric 8 bit and 16 bit standard MR image data-set and validated on real patient’s 16 bit depth MR images collected from local hospitals. The performance of the proposed technique showed improvement over the existing techniques JPEG 2000, JPEG-LS, M-CALIC, JP3D, and CALIC by 40.89%, 34.50%, 32.92%, 22.36%, and 17.25% respectively in terms of Bits per Pixel (BPP).

The compression of the ciphertext of a cryptosystem is desirable considering the dramatic increase in secure data transfer via Internet. In this paper, we propose a simple and universal scheme to compress and decompress the ciphertext of an optical cryptosystem by the aid of deep learning (DL). For compression, the ciphertext is first resized to a relatively small dimension by bilinear interpolation and thereafter condensed by the JPEG2000 standard. For decompression, a well-trained deep neural network (DNN) can be employed to perfectly recover the original ciphertext, in spite of the severe information loss suffered by the compressed file. In contrast with JPEG2000 and JPEG, our proposal can achieve a far smaller size of the compressed file (SCF) while offering comparable decompression quality. In addition, the SCF can be further reduced by compromising the quality of the recovered plaintext. It is also shown that the compression procedure can provide an additional security level, and this may offer new insight into the compressive encryption in optical cryptosystems. Both simulation and experimental results are presented to demonstrate the proposal.

This paper provides a method to build a deep learning image coding system based on inverse problem, choosing a suitable measurement operator to reduce the amount of information transmitted at the sender, and reconstructing the original image by tackling the inverse problem at the receiver. Unlike most compressed sensing (CS) methods, the proposed coding scheme does not rely on sparsity but uses the structural priors of the generative adversarial networks (GAN) to solve the inverse problem. The proposed model trains the GAN to learn a mapping from the latent space to the sample space formed by correlated images on the cloud. Then the measurements are used to localize the optimal latent variable in the representation space which corresponding to the original image in the sample space. The proposed method encodes and transmits the measurements instead of the original image, which greatly reduces the cost of transmission while ensuring the quality of the reconstructed the image at high compression ratios. To the best of our knowledge, this is the first time to introduce the GAN-based inverse problem in the field of the deep image coding area. The experimental results show that the visual quality of the images generated by the proposed scheme is better than the traditional encoding scheme JPEG2000. Especially in the case of extremely high compression ratios, the proposed scheme can still maintain good performance.

The file attached to this record is the author's final peer reviewed version. Mismatches between the precisions of representing the disparity, depth value and rendering position in 3D video systems cause redundancies in depth map representations. In this paper, we propose a highly efficient multiview depth coding scheme based on Depth Histogram Projection (DHP) and Allowable Depth Distortion (ADD) in view synthesis. Firstly, DHP exploits the sparse representation of depth maps generated from stereo matching to reduce the residual error from INTER and INTRA predictions in depth coding. We provide a mathematical foundation for DHP-based lossless depth coding by theoretically analyzing its rate-distortion cost. Then, due to the mismatch between depth value and rendering position, there is a many-to-one mapping relationship between them in view synthesis, which induces the ADD model. Based on this ADD model and DHP, depth coding with lossless view synthesis quality is proposed to further improve the compression performance of depth coding while maintaining the same synthesized video quality. Experimental results reveal that the proposed DHP based depth coding can achieve an average bit rate saving of 20.66% to 19.52% for lossless coding on Multiview High Efficiency Video Coding (MV-HEVC) with different groups of pictures. In addition, our depth coding based on DHP and ADD achieves an average depth bit rate reduction of 46.69%, 34.12% and 28.68% for lossless view synthesis quality when the rendering precision varies from integer, half to quarter pixels, respectively. We obtain similar gains for lossless depth coding on the 3D-HEVC, HEVC Intra coding and JPEG2000 platforms.

In this paper, we propose a new low bit rate scheme that combines the Discrete Wavelet transform (IWT) and Vector Quantization (VQ) for image compression with high Compression Ratio (CR) in order to achieve acceptable quality of output images, Image compression using the combination of DWT willy VQ has been considered in many recent works; here in our paper the DWT was performed on several gray scale standard images, each of (256X256) byte. We used 4 images as the training images of the train VO using LBG algorithm and got the final optimal codebook. Then we used another two images to compare our output image with JPEG 2000 standard winch based on Wavelet and Scalar quantization. Our proposed system used a VO codebook with Variable cod hook size and dimensions were used to compress wavelet Transform) coefficients resulting using Danny wavelet types. Our pro system is slowed so that the best Wavelet types that you can use for compression is COIF 2. Our proposed system gave better PSNR than G2000 standard at high (CR), above than (5L), and this achieved a VQ codebook size (1024 -512) with dimension equal to 256. In the paper a typical system for the application of image compression system and new scientific results are present.

We propose a reversible data hiding (RDH) method in compressible encrypted images called the encryption-then-compression (EtC) images. The proposed method allows us to not only embed a payload in encrypted images but also compress the encrypted images containing the payload. In addition, the proposed RDH method can be applied to both plain images and encrypted ones, and the payload can be extracted flexibly in the encrypted domain or from the decrypted images. Various RDH methods have been studied in the encrypted domain, but they are not considered to be two-domain data hiding, and the resultant images cannot be compressed by using image coding standards, such as JPEG-LS and JPEG 2000. In our experiment, the proposed method shows high performance in terms of lossless compression efficiency by using JPEG-LS and JPEG 2000, data hiding capacity, and marked image quality., Comment: 10 pages

Deep learning has achieved a preliminary success in image compression due to the ability to learn the nonlinear spaces with compact features that training samples belong to. Unfortunately, it is not straightforward for the network based image compression methods to code multiple highly related images. In this paper, we propose a co-prediction based image compression (CPIC) which uses the multi-stream autoencoders to collaboratively code the multiple highly correlated images by enforcing the co-reference constraint on the multi-stream features. Patch samples fed into the multi-stream autoencoder, are generated through corresponding patch matching under permutation, which helps the autoencoder to learn the relationship among corresponding patches from the correlated images. Each stream network consists of encoder, decoder, importance map network and binarizer. In order to guide the allocation of local bit rate of the binary features, the important map network is employed to guarantee the compactness of learned features. A proxy function is used to make the binary operation for the code layer of the autoencoder differentiable. Finally, the network optimization is formulated as a rate distortion optimization. Experimental results prove that the proposed compression method outperforms JPEG2000 up to 1.5 dB in terms of PSNR.

Purpose The joint photographic experts group (JPEG) 2000 image compression system is being used for cultural heritage preservation. The authors are aware of over a dozen of big memory institutions worldwide using this format. This paper aims to review and explain choices for end users to help resolve trade-offs that these users are likely to encounter in practice. Design/methodology/approach The JPEG 2000 format is quite complex and therefore sometimes considered as a preservation risk. A lossy compression is governed by a number of parameters that control compression speed and rate-distortion trade-off. Their inappropriate adjustment may fairly easily lead to sub-optimal compression performance. This paper provides general guidelines for selecting the most appropriate parameters for a specific application. Findings This paper serves as a guide for the preservation of digital heritage in cultural heritage institutions, including libraries, archives and museums. Originality/value This paper serves as a guide for the preservation of digital heritage in cultural heritage institutions, including libraries, archives and museums.

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.

In this paper, has been proposed Algorithm for standard JPEG2000 part one for image compression. The proposed Algorithm was executed by using MATLAB7.11 environment, applied these algorithm on the gray and color images for type of the images natural, medical, Graphics images and remote sensing. Dependence on the Peak Signal-to-Noise Ratio (PSNR) for comparing the result of the proposed Algorithm by using the Daubechies filters 5/3 tap filter and 9/7 tap filter Biothogonal , Another comparison is held concerning the obtained results of the algorithm of ModJPEG and Color-SPECK. Proved the processing results Efficiency performance of proposed Algorithm.

In video compression class projects, students may observe some strange behaviors when using video codecs. Some performance metrics from a mediocre codec such as motion JPEG-2000 (or simply JPEG2000) may have exceptionally high values at certain compression ratios as compared to other high performing codecs. This strange behaviors may be overlooked by instructors and students may never understand why this is happening. In this paper, we will first highlight the strange behaviors. We will then use experiments to systematically determine the root cause. Our experiments show that, if one uses a previously compressed and decompressed video in some compression experiments, then it is highly likely that some strange behaviors will show up. Some advice will be provided to instructors, tutors, and students on how one can prevent such behaviors from occurring.

The communication of reliable data among wireless users that employ multiclass bitstreams to achieve high coding gains has been a tremendous challenge for researchers. In practice, multimedia standards are implemented in wireless technologies to achieve unequal error protection (UEP). These standards employ data partitioning where different levels of importance are assigned to the multimedia content. In this context, in this paper, we provide a generalized UEP framework to evaluate the theoretical error performance bounds of concatenated Reed–Solomon and rate-compatible punctured convolutional (RCPC) codes employing hierarchical 8PSK modulation over both Gaussian and flat Rayleigh wireless fading channels. The contribution of this work is to promote unequal error protection of the multimedia bitstreams without sacrificing channel bandwidth and increasing the computational complexity of the communication system. All the derived theoretical performance bounds are illustrated with numerical examples, and these are simulated using MATLAB Simulink® R2017. Simulation results revealed that with the lowest constraint length of the RCPC codes, near 5-dB difference is achieved between the first- and last-priority subchannels. The proposed unequal protection scheme can be applied to deep-space data transmission systems, digital video broadcasting and wireless JPEG2000 as well as H.265 data transmission systems.

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.

Digital image watermarking algorithms have been designed for intellectual property, copyright protection, medical data management, and other related fields, furthermore, in real-world applications such as official documents, banknotes, etc., they are used to deliver additional information about the documents’ authenticity. In this context, the imperceptible–visible watermarking (IVW) algorithm has been designed as a digital reproduction of the real-world watermarks. This paper presents a new improved IVW algorithm for copyright protection that can deliver additional information to the image content. The proposed algorithm is divided into two stages: in the embedding stage, a human visual system-based strategy is used to embed an owner logotype or a 2D quick response (QR) code as a watermark into a color image, maintaining a high watermark imperceptibility and low image-quality degradation. In the exhibition, a new histogram binarization function approach is introduced to exhibit any watermark with enough quality to be recognized or decoded by any application, which is focused on reading QR codes. The experimental results show that the proposed algorithm can embed one or more watermark patterns, maintaining the high imperceptibility and visual quality of the embedded and the exhibited watermark. The performance evaluation shows that the method overcomes several drawbacks reported in previous algorithms, including geometric and image processing attacks such as JPEG and JPEG2000.

Deep neural networks have been successfully used in image processing and have benefitted image compression technology. On this basis, deeper and more extensive use of compression algorithms in remote sensing applications was investigated in our study. As a general residual block does not effectively utilize the connections between channels and spatial regions in the reconstruction process, an attention mechanism codec framework based on the channel-spatial attention residual block was introduced. Consequently, the network can account for the interdependence between channels and enhance specific spatial areas. Simultaneously, the feature importance map makes the network focus on valuable information, especially for small objects and remote sensing images with many tiny details, which is crucial to describe their contours. Introducing the attention mechanism commonly used in target detection can effectively recover the missing detailed information for remote sensing compressed images. Moreover, to utilize the underlying information of the various scene categories and characterize the feature differences between scenes in remote sensing images, an adaptive scene-aware module was introduced to enhance the feature representation in remote sensing images of different scenes. The experimental results show that the proposed method has superior subjective and objective effects in remote sensing image compression tasks.

Still images have a significant share in the data created today. These images take up a lot of space in their raw form. Image compression algorithms provide a much smaller representation of images in order to solve this problem. The JPEG group offered many alternatives to the JPEG algorithm, which became insufficient due to the increase in resolution and color detail. In this study, lossless compression ratios and encoding-decoding times of JPEG-LS, JPEG 2000, JPEG XR, JPEG XT, JPEG XL and also WebP and PNG algorithms, were examined on selected data sets. For the experiments, images in three categories as SDR, 16-bit and HDR were obtained. In obtained results, JPEG XL gives the most successful results, with the best in compression ratio and good compression and decompression speeds. For 16 bit images, JPEG XR gives the best compression speed and PNG’s only advantage is its decompression speed.

Image compression is one of the potential techniques for image processing. However, the compression also takes a certain amount of time, and some algorithms are not adapted to all images. In order to improve the processing efficiency and performance, this paper studied the relation between image characteristics and Peak Signal to Noise Ratio (PSNR) to predict the compression performance. In this paper, we adopted JPEG2000 algorithm to compress, and PSNR to evaluate the image quality. The statistics of an image contains the values of mean, variance, entropy and others. Then, we drew the relation graph between each statistic calculated and PSNR, and found the statistic which is the most closely related to PSNR. Finally, we derived an explicit expression. Experimental results show that Image Activity Measure (IAM) has the closest relation with PSNR, and the expression has the average relative error of 2% - 3.0%. Meanwhile, it can be simplified by ensuring that the formula error is unchanged basically. Furthermore, we also used other images dataset for verifying the formula. Gray images all can be well predicted for JPEG2000 algorithm when Compression Ratio (CR) is 16. It indicates that a more accurate and simpler IAM-PSNR relation we had obtained. Therefore, we can predict the compression performance before compression so as to select the appropriate compression algorithm and to provide great convenience for subsequent processing.

The JPEG2000 still image compression standard, while providing a remedy for the many shortcomings of its predecessor JPEG, is still slow to establish itself on the Internet. This problem is mainly due to the complexity of the COder-DECoder (CODEC) which implies its non-adoption by large firms and platforms in the field of image acquisition, processing and transmission. Indeed, the encoding and decoding process consumes a lot of CPU, memory and energy resources and takes a lot of computing time. The objective of this paper is to propose a model for decoding jpeg2000 on lightweight devices running on the Android mobile operating system. This implementation uses coroutines, which are a lightweight process model with reduced resource consumption costs compared to conventional AsyncTask threadsets. The model minimizes decoding time while minimizing CPU and memory usage, resulting in a fast and energetically economical decoded image. The results of integrating the coroutines from the main thread into the decoding process instead of the AsyncTask from the main thread produced better performance in terms of computation time, CPU and memory utilization. Indeed, the use of our model has led to a gain of around 23.41% in execution time, 9.8% in CPU utilization rate and 18.56% in memory utilization rate, compared to the model proposed in the literature which uses the threads.

In this paper, factors that influence the lossless compression efficiency of reversible integer-tointeger (ITI) wavelet filters are presented. It is observed that there is a converged region for the bitrate of the compressed subbands. Reaching the converged region, the bitrate increment is equal to log2K bps, where K is the increment in ratio of the coefficient magnitude mean. The advantages of the 5/3 filters for lossless compression are then clearly explained. Anew quantity, Rmn, which measures the efficiency of reversible ITI transforms on flat spectrum inputs, is defined and is used in the new reversible filter design. First, keeping the distinctive merits of the 5/3 filters for lossless compression, the half-band product filter series of the Daubechies wavelet series is selected as the analysis high-pass filters. Next, the analysis low-pass filter is found by experimentally minimizing the Rmn value. Finally, it is determined that the 17/11 filters, which employ the half-band product filter with 6 vanishing moments, have the highest compression efficiency. Coding results show that the new 17/11 filters lead to a bitrate reduction of 1.7% over the 5/3 filters for lossless image compression. With further enhancements on the transform part and combining the enhanced transform with more efficient entropy coding, bitrate reductions of about 3.1% and 4% respectively over the state-of-the-art lossless compression algorithms JPEG-LS and JPEG2000 lossless mode are achieved.

Unlike common 2D images, the light field representation of a scene delivers spatial and angular description which is of paramount importance for 3D reconstruction. Despite the numerous methods proposed for 2D image watermarking, such methods do not address the angular information of the light field. Hence the exploitation of such methods may cause severe destruction of the angular information. In this paper, we propose a novel method for light field watermarking with extensive consideration of the spatial and angular information. Considering the 4D innate of the light field, the proposed method incorporates 4D wavelet for the purpose of watermarking and converts the heavily-correlated channels from RGB domain to YUV. The robustness of the proposed method has been evaluated against common image processing attacks.

Image compression has always been an important topic in the last decades due to the explosive increase of images. The popular image compression formats are based on different transforms which convert images from the spatial domain into compact frequency domain to remove the spatial correlation. In this paper, we focus on the exploration of data-driven transform, Karhunen-Loéve transform (KLT), the kernels of which are derived from specific images via Principal Component Analysis (PCA), and design a high efficient KLT based image compression algorithm with variable transform sizes. To explore the optimal compression performance, the multiple transform sizes and categories are utilized and determined adaptively according to their rate-distortion (RD) costs. Moreover, comprehensive analyses on the transform coefficients are provided and a band-adaptive quantization scheme is proposed based on the coefficient RD performance. Extensive experiments are performed on several class-specific images as well as general images, and the proposed method achieves significant coding gain over the popular image compression standards including JPEG, JPEG 2000, and the state-of-the-art dictionary learning based methods.

The on-board object detection of remote sensing images is of great significance and challenges in both military and civilian fields. To achieved on-board object detection, we require a high-performance and low power hardware architecture. Inspired by the hardware implementation advantages of the lifting scheme in JPEG2000, we introduce the lifting scheme into object detection networks to promote the hardware implementation capability. Based on the equivalence between the lifting scheme and the convolution, we proposed a lifting based FPGA accelerator. The lifting scheme provides a parallel strategy for every convolutional layer to obtain high performance, and high resource utilization. The proposed accelerator can improve the throughput of networks, and promote the utilization efficiency of hardware resources. The experimental results on lifting based YOLOv3-tiny (LS-YOLOv3-tiny) show that the proposed accelerator has better power efficiency than other hardware implementation, and have better DSP efficiency than other FPGA-based designs.

Spectral-spatial transforms (SSTs) change a raw camera image captured using a color filter array (CFA-sampled image) from an RGB color space composed of red, green, and blue components into a decorrelated color space such as YDgCbCr or YDgCoCg color space composed of luma, difference green, and two chroma components. This paper describes three types of wavelet-based SST (WSST) obtained by reorganizing all of the existing SSTs covered in this paper. First, we introduce three types of macropixel SST (MSST) implemented within each 2×2 macropixel. Next, we focus on 2-channel Haar wavelet transforms, which are simple wavelet transforms, and 3-channel Haar-like wavelet transforms in each MSST and replace the Haar and Haar-like wavelet transforms with Cohen-Daubechies-Feauveau (CDF) 5/3 and 9/7 wavelet transforms, which are customized on the basis of the original pixel positions in two-dimensional (2D) space. Although the test data set is not big, in lossless CFA-sampled image compression based on JPEG 2000, the WSSTs improve the bitrates by about 1.67 to 3.17 % compared with not using a transform and the WSSTs that use 5/3 wavelet transforms improve the bitrates by about 0.31 to 0.71 % compared with the best existing SST. Moreover, in lossy CFA-sampled image compression based on JPEG 2000, the WSSTs show about 2.25 to 4.40 dB and 26.04 to 49.35 % in the Bjýntegaard metrics (BD-PSNRs and BD-rates) compared with not using a transform and the WSSTs that use 9/7 wavelet transforms improve the metrics by about 0.13 to 0.40 dB and 2.27 to 4.80 % compared with the best existing SST.

A raw image captured by a color filter array (CFA), such as a Bayer pattern, is usually compressed after demosaicing with some processings (denoising, deblurring, tone-mapping, and so on). However, since photographers, designers, and high-end users prefer to work with the raw image sampled by CFA (referred to as “raw image”) directly, a raw image should be compressed before demosaicing. For effective raw image compression, this study introduces variable macropixel spectral-spatial transforms (VMSSTs), that can successfully decorrelate not only Bayer raw images but any other pure-color (RGB) ones. The proposed VMSSTs are designed by the following two steps: 1) intra-color decorrelation and 2) inter-color decorrelation. In lossless compression with JPEG 2000, compared with methods which do not use transforms, the VMSSTs reduced the average bitrates of three types of CFAs: from approximately 0.09 to 0.12 bpp for the modified Bayer CFA, from 0.25 to 0.65 bpp for the diagonal stripe CFA, and from 0.33 to 0.70 bpp for the Fujifilm X-Trans CFA due to their high color decorrelation efficiency. In addition, in lossy compression with JPEG 2000, compared with a rearranged method, the VMSSTs improved the average bitrates of the Bjontegaard delta by around 3.97%, 14.95%, and 18.65% for each CFA model, respectively. Although a data-dependent adaptive transformation, the Karhunen-Loeve transform (KLT), showed the best performance in lossy compression, the introduced VMSSTs have shown performances comparable to those of the KLT in lossless compression, despite their simple structures.

Deep learning methods provide a platform to segment boundaries within the retina and choroid in OCT images of the posterior eye, with the ultimate goal of having a robust model that works well across a wide range of different datasets. However, since most studies of deep learning methods use datasets exhibiting similar image quality for both training and evaluation, the effect of varied image quality on such methods is not normally explored in the context of OCT image segmentation. An understanding of the effects of image quality factors is vital to determine the robustness of the methods and their ability to be applied in clinical practice where images exhibiting a range of different qualities are encountered. This study examined a range of factors that can affect standard OCT image quality and determined how and why the performance of an existing neural network based segmentation method can subsequently degrade as a result. Three image quality factors (noise, contrast reduction, and gamma correction) all had a negative impact upon performance, while more robust performance was maintained in the presence of both JPEG and JPEG2000 image compression. Improving the method's robustness to each of these degradations is also demonstrated with marked performance improvements identified by applying a fine-tuning approach to the network. This study improves our understanding of the effect of OCT image degradation on neural network performance, the effect that fine-tuning with poor-image quality data has on the network and highlights the benefit and importance of training resilient models using data augmentation.

The detection of data integrity violations is one of the main issues for information security professionals. In particular, this problem is relevant for digital images (DI), which this work is dedicated to. Block processing of DI has become typical for modern steganographic algorithms, and it is widely used today. Block processing is any processing that involves saving the resulting image in lossy formats (JPEG, JPEG2000). It is local image processing that occurs during cloning and photo editing. In this regard, the aim of the work is to provide effective detection of the DI integrity violations due to its block processing, regardless of its specific type by developing an appropriate method. The method is based on the results obtained by the author previously through research studies of the formal parameters of DI matrix blocks. The proposed method and its algorithmic implementation (SA) have no analogues, that is confirmed by data obtained from open sources since existing methods for detecting image integrity violations are usually designed for specific perturbations. The paper presents the results of a computational experiment, which has confirmed the high efficiency of the developed algorithm in the conditions of all considered perturbations, including block processing. Comparison of the SA effectiveness with analogues was carried out under conditions of specific perturbations. In all cases considered, the efficiency of the developed algorithm is comparable with the best of modern analogues. When using the SA as a steganalysis algorithm for detecting the LSB-embeddings in containers saved with losses, it remains effective under conditions of low capacity of the hidden communication channel (0,01 bit/pixel), while most of analogues algorithms are inoperative.

In modern communication systems, transmission of data, particularly that of images is very important. Unfortunately, the process is prone to different kinds of degradations. For instance, the large traffic on the network makes the reduction of data volume and storage imperative. This is achieved through compression techniques such as JPEG and JPEG2000, at the expense of image quality, unfortunately. Image quality also deteriorates on account of the state of the transmission channel and the amount of losses occurring. Another common type of image degradation is blurring. At the receiving end, identifying the degradation is an important requirement for applications like blind image denoising so that appropriate techniques for removal may be adopted. Since the undegraded original image is usually not available, no-reference methods are not only attractive but also essential. This article proposes a novel no-reference method which can clearly distinguish between noise, compression and blurring. This is achieved in two stages – the first distinguishes between noise and compression/blurring while the second distinguishes between compression and blurring. Finally, a quantification of the degradation suffered is also carried out, which forms another important contribution of the approach. The primary tool employed is the Discrete Wavelet Transform (DWT) and the differential behaviour of the image DWT coefficients to noise, compression and blurring, at various levels of resolution. The use of the properties of the inter-level coefficients make this approach unique in nature. These properties have been explored and analyzed in detail. A large number of simulations using images from popular databases, establish the usefulness of the approach.

While deep learning image compression methods have shown an impressive coding performance, most of them output a single-optimized-compression rate using a trained-specific network. However, in practice, it is essential to support the variable rate compression or meet a target rate with a high-coding performance. This paper proposes a novel image compression method, making it possible for a single convolutional neural network (CNN) model to generate the variable rate efficiently with an optimized rate-distortion (RD) performance. The method consists of CNN-based multi-scale decomposition transform and content adaptive rate allocation. Specifically, the transform network is learned to decompose the input image into several scales of representations while optimizing the RD performance for all scales. Rate allocation algorithms for two typical scenarios are provided to determine the optimal scale of each image block for a given target rate or quality factor. For a target rate, the allocation is adaptive based on content complexity. In addition, for a target quality factor which indicates a tradeoff between the rate and the quality, the optimal scale is determined by minimizing the RD cost. The experimental results have shown that our method has outperformed the JPEG2000 and BPG standards with high efficiency and the state-of-the-art RD performance as measured by the multi-scale structural similarity index metric. Moreover, our method can strictly control the rate to generate the target compression result.

With the rapid increase of optical remote sensing (RS) images, on-orbit specific target detection has been facing more and more challenges: limited processing environment, wireless bandwidth, space, power and storage. To resolve this issue, visual characteristics in the compressed domain are exploited to represent and recognise specific objects. In this study, a new saliency method and an airfield detection framework in the JPEG2000 compressed domain is proposed. First, the authors exploit the header information in raw JPEG2000 code streams to generate structural saliency map, and a great deal of non-structural regions in RS images are eliminated. Second, airfield candidates are extracted from low frequency wavelet sub-bands using line segment detection and the parallel density model. Finally, the geometrical properties of airfield candidates are represented by the speed-up robust features descriptor, and the support vector machine classifier is utilised to gain the final detection results. The proposed framework is evaluated on self-made dataset. Compared with the existing relevant state-of-the-art approaches, the proposed method reduces the processing time by 60% while guaranteeing the detection rate.

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.

In this paper, we are interested in the compression of image sets or video with considerable changes in illumination. We develop a framework to decompose frames into illumination fields and texture in order to achieve sparser representations of frames which is beneficial for compression. Illumination variations or contrast ratio factors among frames are described by a full resolution multiplicative field. First, we propose a Lifting-based Illumination Adaptive Transform (LIAT) framework which incorporates illumination compensation to temporal wavelet transforms. We estimate a full resolution illumination field, taking heed of its spatial sparsity by a rate-distortion (R-D) driven framework. An affine mesh model is also developed as a point of comparison. We find the operational coding cost of the subband frames by modeling a typical t + 2D wavelet video coding system. While our general findings on R-D optimization are applicable to a range of coding frameworks, in this paper, we report results based on employing JPEG 2000 coding tools. The experimental results highlight the benefits of the proposed R-D driven illumination estimation and compensation in comparison with alternative scalable coding methods and non-scalable coding schemes of AVC and HEVC employing weighted prediction.

This paper introduces a new lossy approach for compression of cartoon images. The image is firstly partitioned into regions of roughly the same colour. The chain codes are then determined of all regions. The sequence of the obtained chain code symbols is transformed with the Burrows-Wheeler Transform, Move-To-Front transform, and compressed with Run-Length Encoding. In the final step, an arithmetic encoder may be used to compress the obtained binary stream additionally. The proposed algorithm is asymmetric, meaning that the decompression does not reverse all the steps of the compression procedure. The experimental results have shown that the described method produces considerably better compression ratios than JPEG, JPEG2000, WebP, SPIHT, PNG, and two of the algorithms specialised in compression of cartoon images: the algorithm using quad-tree, and RS-LZ algorithm.

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 digital holographic data with respect to conventional photographic data require a comprehension of the performance and limitations of current image and video standard techniques. This paper proposes an architecture for objective evaluation of the performances of state-the-art compression techniques applied to digital holographic data.

Panchromatic images of space optical remote sensors effectively demonstrate the shape, structure, and texture of landscape objects. Conventional compressors, such as JPEG2000 and Consultative Committee for Space Data Systems-Image Data Compression (CCSDS-IDC), widely use discrete wavelet transforms (DWTs) as sparse multiresolution representations of remote sensing panchromatic images (RSPIs). However, many large amplitude and high-frequency coefficients can be produced when RSPIs project onto a DWT basis, which are detrimental to the subsequent encoding process. Therefore, using only DWT to perform sparse representation is not an optimal solution for RSPIs. In this study, we propose a low-complexity compression approach based on a dual-loop, double-bases post-transform combined with bit-plane encoding (BPE) for RSPIs. First, a DWT is applied to RSPIs to perform the transform operations. Then, from the computed DWT coefficients a post-transform with a double base (discrete cosine transform and Hadamard transform) is performed. One of the double bases can be used for the high bit rate and the other can be used for the low bit rate. The best post-transform is selected based on a p-norms approach. The post-transformed coefficients are reorganized into tree structures to perform encoding using BPE. We use dual loop (basis loop and rate loop) to control the coding process. BPE results are fed back to select the basis used by post-transform. The best post-transform results are fed forward to allocate the bit rate for each coding segment. The experimental results of on-board RSPIs show that the proposed approach improves the PSNR by 0.6 dB to 1.1 dB compared to that of the CCSDS-IDC method with a small increase in complexity. The compression performance of the proposed method obtains similar results, but lower complexity to that of JPEG2000-based coders.