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The High Throughput JPEG 2000 (HTJ2K) is Part 15 of the JPEG 2000 family of standards, and it is published as ITUTT. 814 | ISO/IEC 15444-15. HTJ2K significantly improves coding throughput to image compression pipelines with JPEG 2000 technology. In this paper, an open-source C++ implementation of HTJ2K is proposed. The implementation principles are “easy to understand what is done inside HTJ2K codec” and “easy to use.” Unlike the existing open-source implementation, our decoder is fully compliant with the conformance testing defined in the new edition of JPEG 2000 Part 4. The experimental results demonstrate the proposed codec performs well in terms of encoding/decoding speed.

In a world teeming with digital images, the credibility of visual data has become of paramount importance. While it is now simpler than ever to manipulate an image for malicious purposes such as misinformation, the tools for detecting such alterations have predominantly been developed for either uncompressed or JPEG-compressed natural images. However, medical and satellite imagery, domains where the potential for fraud is high, often use a different compression format -- JPEG 2000. We present a JPEG 2000 Anomaly Detection Estimator via Offset of Wavelet Localization -- the Jade Owl --, a novel method for detecting forgeries in JPEG 2000 images by analyzing the consistency of traces left by its compression. Our technique hinges on the premise that the wavelet coefficients of a JPEG 2000 image are lower when the same offset is applied during the wavelet transform than they are when the offset is different. By employing this principle locally, we're able to detect regions with significantly different offsets, indicating potential forgeries such as copy-move. An accompanying \textit{a contrario} model further refines this detection to make automatic detections while controlling false positives. To evaluate the method, we've created a unique dataset of JPEG 2000 forgeries. This novel approach significantly paves the way for JPEG 2000 image forensics, introducing a sensitive and efficient tool for authenticity verification in critical sectors such as healthcare and satellite imagery., This work has received funding by the European Union under the Horizon Europe vera.ai project, grant agreement number 101070093, and by the ANR under the APATE project, grant number ANR-22-CE39-0016. Centre Borelli is also a member of Université Paris Cité, SSA and INSERM., {"references":["A. Ghoneim, G. Muhammad, S. U. Amin, and B. 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Le Hegarat-Mascle, \"An a-contrario approach for subpixel change detection in satellite imagery,\" IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, 2010.","T. Ehret, A. Davy, M. Delbracio, and J.-M. Morel, \"How to Reduce Anomaly Detection in Images to Anomaly Detection in Noise,\" Image Processing On Line, vol. 9, 2019.","T. Dagobert, R. Grompone von Gioi, C. de Franchis, J.-M. Morel, and C. Hessel, \"Cloud Detection by Luminance and Inter-band Parallax Analysis for Pushbroom Satellite Imagers,\" Image Processing On Line, vol. 10, 2020.","L. Moisan, P. Moulon, and P. Monasse, \"Automatic Homographic Registration of a Pair of Images, with A Contrario Elimination of Outliers,\" Image Processing On Line, vol. 2, 2012.","N. Mandroux, T. Dagobert, S. Drouyer, and R. Grompone von Gioi, \"Single Date Wind Turbine Detection on Sentinel-2 Optical Images,\" Image Processing On Line, vol. 12, 2022.","A. Buades, R. Grompone von Gioi, and J. Navarro, \"Contours, Corners and T-Junctions Detection Algorithm,\" Image Processing On Line, vol. 8, 2018.","C. Aguerrebere, P. Sprechmann, P. Muse, and R. Ferrando, \"A-contrario localization of epileptogenic zones in spect images,\" in 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009.","E. Aldea and S. L. Hégarat-Mascle, \"Robust crack detection for un- manned aerial vehicles inspection in an a-contrario decision framework,\" JEI, 2015.","R. Grompone von Gioi, C. Hessel, T. Dagobert, J.-M. Morel, and C. de Franchis, \"Ground Visibility in Satellite Optical Time Series Based on A Contrario Local Image Matching,\" Image Processing On Line, vol. 11, 2021.","R. Grompone von Gioi, J. Jakubowicz, J.-M. Morel, and G. Randall, \"Lsd: A fast line segment detector with a false detection control,\" IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, 2010.","R. Grompone von Gioi, J. Jakubowicz, J.-M. Morel, and G. Randall, \"LSD: a Line Segment Detector,\" IPOL, vol. 2, pp. 35–55, 2012. [32] R. Grompone von Gioi and G. Randall, \"Unsupervised Smooth Contour Detection,\" Image Processing On Line, vol. 6, 2016.","A. Gómez, G. Randall, and R. Grompone von Gioi, \"A Contrario 3D Point Alignment Detection Algorithm,\" Image Processing On Line, vol. 7, 2017.","X. Huang, W. Yang, H. Zhang, and G.-S. Xia, \"Automatic ship detection in sar images using multi-scale heterogeneities and an a contrario decision,\" Remote Sensing, 2015.","J. Lezama, G. Randall, J.-M. Morel, and R. Grompone von Gioi, \"An Unsupervised Point Alignment Detection Algorithm,\" Image Processing On Line, vol. 5, 2015.","J.-L. Lisani and S. Ramis, \"A Contrario Detection of Faces with a Short Cascade of Classifiers,\" Image Processing On Line, vol. 9, 2019.","J.-L. Lisani, S. Ramis, and F. J. Perales, \"A contrario detection of faces: A case example,\" SIAM Journal on Imaging Sciences, vol. 10, 2017.","Q. 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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.

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.

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.

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.

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

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 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.

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.

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.

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.

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.