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Image and video compression : fundamentals, techniques, and applications
\"Preface This book is intended primarily for courses in image compression techniques for undergraduate through postgraduate students, research scholars, and engineers working in the field. It presents the basic concepts and technologies in a student-friendly manner. The major techniques in image compression are explained with informative illustrations, and the concepts are evolved from the basics. Practical implementation is demonstrated with MATLAB
Book
Towards an Efficient Remote Sensing Image Compression Network with Visual State Space Model
In the past few years, deep learning has achieved remarkable advancements in the area of image compression. Remote sensing image compression networks focus on enhancing the similarity between the input and reconstructed images, effectively reducing the storage and bandwidth requirements for high-resolution remote sensing images. As the network’s effective receptive field (ERF) expands, it can capture more feature information across the remote sensing images, thereby reducing spatial redundancy and improving compression efficiency. However, the majority of these learned image compression (LIC) techniques are primarily CNN-based and transformer-based, often failing to balance the global ERF and computational complexity optimally. To alleviate this issue, we propose a learned remote sensing image compression network with visual state space model named VMIC to achieve a better trade-off between computational complexity and performance. Specifically, instead of stacking small convolution kernels or heavy self-attention mechanisms, we employ a 2D-bidirectional selective scan mechanism. Every element within the feature map aggregates data from multiple spatial positions, establishing a globally effective receptive field with linear computational complexity. We extend it to an omni-selective scan for the global-spatial correlations within our Channel and Global Context Entropy Model (CGCM), enabling the integration of spatial and channel priors to minimize redundancy across slices. Experimental results demonstrate that the proposed method achieves superior trade-off between rate-distortion performance and complexity. Furthermore, in comparison to traditional codecs and learned image compression algorithms, our model achieves BD-rate reductions of −4.48%, −9.80% over the state-of-the-art VTM on the AID and NWPU VHR-10 datasets, respectively, as well as −6.73% and −7.93% on the panchromatic and multispectral images of the WorldView-3 remote sensing dataset.
Journal Article
Image compression and encryption algorithm based on uniform non-degeneracy chaotic system and fractal coding
This paper focuses on the design of chaotic image compression encryption algorithms. Firstly, we design a uniform non-degenerate chaotic system based on nonlinear filters and the feed-forward and feed-back structure. Theoretical and experimental analyses indicate that the system can avoid the drawbacks of the existing chaotic systems, such as chaos degradation, uneven trajectory distribution, and weak chaotic behavior. In addition, our chaotic system can produce chaotic sequences with good pseudo-random characteristics. Then, we propose a fractal image compression algorithm based on adaptive horizontal or vertical (HV) partition by improving the baseline HV partition and the time-consuming global matching algorithm. The algorithm does not need to implement time-consuming global matching operations. In addition, analysis results demonstrate that our fractal image compression algorithm can reconstruct the original image with high quality under ultra-high compression ratios. Finally, to protect the confidentiality of images, we propose a chaotic fractal image compression and encryption algorithm by using our chaotic system and fractal image compression algorithm. The algorithm achieves excellent diffusion and confusion abilities without using the hash value of plain images. Therefore, it avoids the failure of decryption caused by the tampering of hash value during the transmission process, and can well resist differential attacks and chosen-ciphertext attacks. In addition, simulation results show the algorithm is efficient and robust.
Journal Article
Remote Sensing Image Compression Based on the Multiple Prior Information
Learned image compression has achieved a series of breakthroughs for nature images, but there is little literature focusing on high-resolution remote sensing image (HRRSI) datasets. This paper focuses on designing a learned lossy image compression framework for compressing HRRSIs. Considering the local and non-local redundancy contained in HRRSI, a mixed hyperprior network is designed to explore both the local and non-local redundancy in order to improve the accuracy of entropy estimation. In detail, a transformer-based hyperprior and a CNN-based hyperprior are fused for entropy estimation. Furthermore, to reduce the mismatch between training and testing, a three-stage training strategy is introduced to refine the network. In this training strategy, the entire network is first trained, and then some sub-networks are fixed while the others are trained. To evaluate the effectiveness of the proposed compression algorithm, the experiments are conducted on an HRRSI dataset. The results show that the proposed algorithm achieves comparable or better compression performance than some traditional and learned image compression algorithms, such as Joint Photographic Experts Group (JPEG) and JPEG2000. At a similar or lower bitrate, the proposed algorithm is about 2 dB higher than the PSNR value of JPEG2000.
Journal Article
DCT-based medical image compression using machine learning
Medical images need to be efficiently compressed before transmission and storage, due to the storage capacity and constrained bandwidth issues. An ideal image compression system must yield a high compression ratio with good quality compressed images. Machine learning models are implemented to perform tasks, whereas humans have difficulties in completing. For instance, an optimum compression ratio could be suggested considering the details on an X-ray image. In this paper, machine learning algorithms are trained to relate the medical image contents to their compression ratio. Once trained, the optimum DCT compression ratio of the X-ray images is chosen upon presenting an image to the network. Experimental results showed that the radial basis function neural network learning algorithm can be efficiently used to classify the optimum compression ratio for the X-ray images while maintaining high image quality. The radial basis function neural network learning algorithm can be efficiently used to classify optimum compression ratio, considering optimum compression deviation with various levels of accuracy. The experiments are done using two compression scenarios considering the ratio of training and testing. Two different scenarios are defined and discussed. When proposed scenario 1 is considered, gradient boosting algorithm and support vector machine achieved the highest recognition rate of 79.16%; however, radial basis function neural network achieved the highest recognition rate of 90.625%, whereas when proposed scenario 2 considered with an accuracy rate of 89% as optimum compression deviation 1 is noted.
Journal Article
Combining Image Space and q-Space PDEs for Lossless Compression of Diffusion MR Images
Diffusion MRI is a modern neuroimaging modality with a unique ability to acquire microstructural information by measuring water self-diffusion at the voxel level. However, it generates huge amounts of data, resulting from a large number of repeated 3D scans. Each volume samples a location in q-space, indicating the direction and strength of a diffusion sensitizing gradient during the measurement. This captures detailed information about the self-diffusion and the tissue microstructure that restricts it. Lossless compression with GZIP is widely used to reduce the memory requirements. We introduce a novel lossless codec for diffusion MRI data. It reduces file sizes by more than 30% compared to GZIP and also beats lossless codecs from the JPEG family. Our codec builds on recent work on lossless PDE-based compression of 3D medical images, but additionally exploits smoothness in q-space. We demonstrate that, compared to using only image space PDEs, q-space PDEs further improve compression rates. Moreover, implementing them with finite element methods and a custom acceleration significantly reduces computational expense. Finally, we show that our codec clearly benefits from integrating subject motion correction and slightly from optimizing the order in which the 3D volumes are coded.
Journal Article
Scalable image compression algorithms with small and fixed-size memory
The SPIHT image compression algorithm is characterized by low computational complexity, good performance, and the production of a quality scalable bitstream that can be decoded at several bit-rates with image quality enhancement as more bits are received. However, it suffers from the enormous computer memory consumption due to utilizing linked lists of size of about 2–3 times the image size. In addition, it does not exploit the multi-resolution feature of the wavelet transform to produce a resolution scalable bitstream by which the image can be decoded at numerous resolutions (sizes). The Single List SPIHT (SLS) algorithm resolved the high memory problem of SPIHT by using only one list of fixed size equals to just 1/4 the image size, and state marker bits with an average of 2.25 bits/pixel. This paper introduces two new algorithms that are based on SLS. Like SLS, the first algorithm also produces a quality scalable bitstream. However, it has lower time complexity and better performance than SLS. The second algorithm, which is the major contribution of the work, upgrades the first algorithm to produce a bitstream that is both quality and resolution scalable. As such, the algorithm is very suitable for the modern heterogeneous nature of the internet users to satisfy their different capabilities and desires in terms of image quality and resolution.
Journal Article
Multispectral Transforms Using Convolution Neural Networks for Remote Sensing Multispectral Image Compression
A multispectral image is a three-order tensor since it is a three-dimensional matrix, i.e., one spectral dimension and two spatial position dimensions. Multispectral image compression can be achieved by means of the advantages of tensor decomposition (TD), such as Nonnegative Tucker Decomposition (NTD). Unfortunately, the TD suffers from high calculation complexity and cannot be used in the on-board low-complexity case (e.g., multispectral cameras) that the hardware resources and power are limited. Here, we propose a low-complexity compression approach for multispectral images based on convolution neural networks (CNNs) with NTD. We construct a new spectral transform using CNNs, where the CNNs are able to transform the three-dimension spectral tensor from large-scale to a small-scale version. The NTD resources only allocate the small-scale three-dimension tensor to improve calculation efficiency. We obtain the optimized small-scale spectral tensor by the minimization of original and reconstructed three-dimension spectral tensor in self-learning CNNs. Then, the NTD is applied to the optimized three-dimension spectral tensor in the DCT domain to obtain the high compression performance. We experimentally confirmed the proposed method on multispectral images. Compared to the case that the new spectral tensor transform with CNNs is not applied to the original three-dimension spectral tensor at the same compression bit-rates, the reconstructed image quality could be improved. Compared with the full NTD-based method, the computation efficiency was obviously improved with only a small sacrifices of PSNR without affecting the quality of images.
Journal Article
Image compression and encryption algorithm based on 2D compressive sensing and hyperchaotic system
Aiming at the security and efficiency problems in the process of image transmission, an image compression–encryption scheme based on 2D compressive sensing and hyperchaotic system is proposed in this paper. First, we construct a hyperchaotic system with more complex chaotic behavior, which is used to construct the measurement matrix of compressive sensing. Then, two-dimensional compressive sensing is used to compress the image. Compared with one-dimensional sensing, it achieves faster execution efficiency and better image reconstruction quality. Finally, to improve the encryption security, we use the multiplicative inverse operation in the finite domain to diffuse the cipher image after compressive sensing. The experimental simulation results show that the algorithm in this paper has higher execution efficiency, better image reconstruction quality, great security and robustness.
Journal Article
Quality Control for the BPG Lossy Compression of Three-Channel Remote Sensing Images
This paper deals with providing the desired quality in the Better Portable Graphics (BPG)-based lossy compression of color and three-channel remote sensing (RS) images. Quality is described by the Mean Deviation Similarity Index (MDSI), which is proven to be one of the best metrics for characterizing compressed image quality due to its high conventional and rank-order correlation with the Mean Opinion Score (MOS) values. The MDSI properties are studied and three main areas of interest are determined. It is shown that quite different quality and compression ratios (CR) can be observed for the same values of the quality parameter Q that controls compression, depending on the compressed image complexity. To provide the desired quality, a modified two-step procedure is proposed and tested. It has a preliminary stage carried out offline (in advance). At this stage, an average rate-distortion curve (MDSI on Q) is obtained and it is available until the moment when a given image has to be compressed. Then, in the first step, an image is compressed using the starting Q determined from the average rate-distortion curve for the desired MDSI. After this, the image is decompressed and the produced MDSI is calculated. In the second step, if necessary, the parameter Q is corrected using the average rate-distortion curve, and the image is compressed with the corrected Q. Such a procedure allows a decrease in the MDSI variance by around one order after two steps compared to variance after the first step. This is important for the MDSI of approximately 0.2–0.25 corresponding to the distortion invisibility threshold. The BPG performance comparison to some other coders is performed and examples of its application to real-life RS images are presented.
Journal Article