Quantization Matrix Research Articles

JPEG Quantized Coefficient Recovery via DCT Domain Spatial-Frequential Transformer.

JPEG compression adopts the quantization of Discrete Cosine Transform (DCT) coefficients for effective bit-rate reduction, whilst the quantization could lead to a significant loss of important image details. Recovering compressed JPEG images in the frequency domain has recently garnered increasing interest, complementing the multitude of restoration techniques established in the pixel domain. However, existing DCT domain methods typically suffer from limited effectiveness in handling a wide range of compression quality factors or fall short in recovering sparse quantized coefficients and the components across different colorspaces. To address these challenges, we propose a DCT domain spatial-frequential Transformer, namely DCTransformer, for JPEG quantized coefficient recovery. Specifically, a dual-branch architecture is designed to capture both spatial and frequential correlations within the collocated DCT coefficients. Moreover, we incorporate the operation of quantization matrix embedding, which effectively allows our single model to handle a wide range of quality factors, and a luminance-chrominance alignment head that produces a unified feature map to align different-sized luminance and chrominance components. Our proposed DCTransformer outperforms the current state-of-the-art JPEG artifact removal techniques, as demonstrated by our extensive experiments.

АНАЛІЗ АЛГОРИТМІВ СТИСНЕННЯ ЗОБРАЖЕНЬ ІЗ ВТРАТАМИ

The article discusses and conducts an analytical review of lossy image compression algorithms. Substantiated the relevance of the research with the help of statistical data. Considered and analyzed the color subsampling method. Reviewed, described, and analyzed the color quantization method, in particular, existing studies on the application of color quantization in combination with the discrete cosine transform. Highlighted the shortcomings of the existing research and formulated the possibility of further research using an expanded sample of images. Considered and analyzed in detail the compression based on the discrete cosine transform. Singled out the search for optimal quantization matrices as a promising direction of further research on improving the efficiency of the application of discrete cosine transformation. Highlighted the adaptive allocation of larger, multiples of the standard data blocks as a promising direction of research. Considered and analyzed the image compression method based on the wavelet transform. Formulated the direction of further research on the use of wavelets other than Cohen-Dobechy-Feuvo and LeGall-Tabatabay wavelet for image compression. Considered and analyzed the method of fractal compression. Formulated directions for further research, such as limiting the search depth and applying fractal compression in combination with discrete cosine transformation. Summarized directions for further research to improve the functional characteristics of the considered algorithms. The main scientific result of the conducted research is the selection of a list of promising research topics that will allow increasing the amount of data on methods, models and means of image compression. The practical value of the research is that it contains a list of research topics that can be used by researchers as material for further research.

HIGH DYNAMIC RANGE IMAGE COMPRESSION ON COMMODITY HARDWARE FOR REAL-TIME MAPPING APPLICATIONS

Abstract. This paper describes a lossy compression scheme for high dynamic range graylevel and color imagery for data transmission purposes in real-time mapping scenarios. The five stages of the implemented non-standard transform coder are written in portable C++ code and do not require specialized hardware to run. Storage space occupied by the bitmaps is reduced via a color space change, 2D integer discrete cosine transform (DCT) approximation, coefficient quantization, two-size run-length encoding and dictionary matching hinged on the LZ4 algorithm. Quantization matrices to eliminate insignificant DCT coefficients are derived from a representative image set through genetic optimization. The underlying fitness function incorporates the obtained output size, classic image quality metrics and the unique color count. Together with a zone-based adaptation mechanism, this allows to specify target bitrates instead of percentage values or abstract quality factors for the reduction rate to be directly matched to the available communication channel capacities. Results on a camera control unit of a fixed-wing unmanned aircraft system built around entry-level PC hardware revealed single-thread compression and decompression throughputs of several hundred mebibytes per second for full-swing 16 and 32 bit RGB imagery at medium compression ratios. A degradation in image quality compared to popular compression libraries could be identified, however, at acceptable levels statistically and visually.

Dipper Donkin Quantized Matrix Algebra and Reflection Equation Algebra at Root of Unity

Dipper Donkin Quantized Matrix Algebra and Reflection Equation Algebra at Root of Unity

Detecting Double JPEG Compressed Color Images via an Improved Approach

Detecting double Joint Photographic Experts Group (JPEG) compression for color images is vital in the field of image forensics. In previous researches, there have been various approaches to detecting double JPEG compression with different quantization matrices. However, the detection of double JPEG color images with the same quantization matrix is still a challenging task. An effective detection approach to extract features is proposed in this paper by combining traditional analysis with Convolutional Neural Networks (CNN). On the one hand, the number of nonzero pixels and the sum of pixel values of color space conversion error are provided with 12-dimensional features through experiments. On the other hand, the rounding error, the truncation error and the quantization coefficient matrix are used to generate a total of 128-dimensional features via a specially designed CNN. In such a CNN, convolutional layers with fixed kernel of and Dropout layers are adopted to prevent overfitting of the model, and an average pooling layer is used to extract local characteristics. In this approach, the Support Vector Machine (SVM) classifier is applied to distinguish whether a given color image is primarily or secondarily compressed. The approach is also suitable for the case when customized needs are considered. The experimental results show that the proposed approach is more effective than some existing ones when the compression quality factors are low.

An Algorithm to Reduce Compression Ratio in Multimedia Applications

In recent years, it has been evident that internet is the most effective means of transmitting information in the form of documents, photographs, or videos around the world. The purpose of an image compression method is to encode a picture with fewer bits while retaining the decompressed image’s visual quality. During transmission, this massive data necessitates a lot of channel space. In order to overcome this problem, an effective visual compression approach is required to resize this large amount of data. This work is based on lossy image compression and is offered for static color images. The quantization procedure determines the compressed data quality characteristics. The images are converted from RGB to International Commission on Illumination CIE La*b*; and YCbCr color spaces before being used. In the transform domain, the color planes are encoded using the proposed quantization matrix. To improve the efficiency and quality of the compressed image, the standard quantization matrix is updated with the respective image block. We used seven discrete orthogonal transforms, including five variations of the Complex Hadamard Transform, Discrete Fourier Transform and Discrete Cosine Transform, as well as thresholding, quantization, de-quantization and inverse discrete orthogonal transforms with CIE La*b*; and YCbCr to RGB conversion. Peak to signal noise ratio, signal to noise ratio, picture similarity index and compression ratio are all used to assess the quality of compressed images. With the relevant transforms, the image size and bits per pixel are also explored. Using the (n, n) block of transform, adaptive scanning is used to acquire the best feasible compression ratio. Because of these characteristics, multimedia systems and services have a wide range of possible applications.

Video Error-Resilience Encoding and Decoding Based on Wyner-Ziv Framework for Underwater Transmission

Limited by the characteristics of underwater acoustic channels, the video transmission applications targeting deep-sea detection and operation tasks are facing severe challenges such as network failure and high delay, resulting in loss of video details, color distortion, blurring, and even bit errors, which seriously affect decoding quality of the video transmission and reception. In order to solve the problems of deep-sea long-distance wireless communication, this paper proposes an improved Wyner-Ziv coding scheme (UnderWater-WZ) for video transmission through acoustic channels. The implementation process includes controlling error range by using MJPEG coding, combining motion compensation time interpolation with calibration information to generate high-quality side information. And intraframe quantization matrix is designed to weaken the change of video scene. The experimental results show that under the highest packet loss rate of 20%, this scheme can achieve 2.6~3.5 dB improvements in terms of video reconstruction compared to the previous methods, which is close to the error-free level.

CNN-based first quantization estimation of double compressed JPEG images

Multiple JPEG compressions leave artifacts in digital images: residual traces that could be exploited in forensics investigations to recover information about the device employed for acquisition or image editing software. In this paper, a novel First Quantization Estimation (FQE) algorithm based on convolutional neural networks (CNNs) is proposed. In particular, a solution based on an ensemble of CNNs was developed in conjunction with specific regularization strategies exploiting assumptions about neighboring element values of the quantization matrix to be inferred. Mostly designed to work in the aligned case, the solution was tested in challenging scenarios involving different input patch sizes, quantization matrices (both standard and custom) and datasets (i.e., RAISE and UCID collections). Comparisons with state-of-the-art solutions confirmed the effectiveness of the presented solution demonstrating for the first time to cover the widest combinations of parameters of double JPEG compressions.

Detecting Steganography in JPEG Images Recompressed With the Same Quantization Matrix

JPEG steganalysis aims to detect stego JPEG images. For some robust steganography methods, in order to enhance stego images’ robustness of resisting JPEG recompression from lossy channel such SNS or photo sharing websites, steganographer may intentionally recompress cover image several times with quantization matrix of targeted channel, which thereby make it possible to transmit stego data in such channel for better disguise. In addition, there are huge number of cover JPEG images may be recompressed for various reasons, such as processing by some tools. Thus a better steganalysis method for such images is needed. In this paper, we investigate the steganalysis method for images recompressed with the same quantization matrix, namely, discriminate recompressed JPEG cover images and its stego images. We present some observed phenomenon on recompressed JPEG images, and design methods to enhance the sensitivity of feature based and deep model based steganalysis methods for this task. To verify their effectiveness with different acquisition of recompression prior-knowledge, we conduct experiments in various settings including conventional setting and mixing samples of different recompressing times in training. Their results demonstrate that the proposed method can notably improve detection accuracy on recompressed JPEG images.

Direction-of-Arrival Estimation for Nested Array Using Mixed-Resolution ADCs

In this letter, an effective method for estimating direction-of-arrival (DOA) using the nested array via mixed-resolution (MR) quantization is developed. First, we derive the output signal model of the nested array under MR quantization, and utilize MR quantized data to construct a quantized covariance matrix. Subsequently, we recover an unquantized augmented covariance matrix by formulating a rank-minimization problem in the coarray domain. Finally, we perform DOA estimation using the recovered covariance matrix. The proposed method can achieve excellent performance close to that of the existing methods using high-resolution data. Simulation results demonstrate the validity of our approach.

Walsh–Hadamard Kernel Feature-Based Image Compression Using DCT with Bi-Level Quantization

To meet the high bit rate requirements in many multimedia applications, a lossy image compression algorithm based on Walsh–Hadamard kernel-based feature extraction, discrete cosine transform (DCT), and bi-level quantization is proposed in this paper. The selection of the quantization matrix of the block is made based on a weighted combination of the block feature strength (BFS) of the block extracted by projecting the selected Walsh–Hadamard basis kernels on an image block. The BFS is compared with an automatically generated threshold for applying the specific quantization matrix for compression. In this paper, higher BFS blocks are processed via DCT and high Q matrix, and blocks with lower feature strength are processed via DCT and low Q matrix. So, blocks with higher feature strength are less compressed and vice versa. The proposed algorithm is compared to different DCT and block truncation coding (BTC)-based approaches based on the quality parameters, such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) at constant bits per pixel (bpp). The proposed method shows significant improvements in performance over standard JPEG and recent approaches at lower bpp. It achieved an average PSNR of 35.61 dB and an average SSIM of 0.90 at a bpp of 0.5 and better perceptual quality with lower visual artifacts.

Detecting Aligned Double JPEG Compressed Color Image With Same Quantization Matrix Based on the Stability of Image

Joint photographic experts group (JPEG) compression is widely used in image processing and computer vision. Detecting double compressed JPEG images is a common problem in forensics and detecting compressed images with the same quantization matrix remains a challenging task. However, most existing methods were designed for detection in grayscale images and cannot fully use the unique characteristics of color images (such as the relationship between channels and color information). In addition, the performance of existing methods is unsatisfactory for low JPEG quality factors and in cross detection experiments. To solve these problems, we analyze the stability of a color image to obtain the convergence error and transposition error. According to the convergence characteristics of color JPEG images, the continuous compression by the same quantization matrix can make the JPEG image tend to be stable. The final stable state and the convergence process are determined by the number of compressions of the original image. Thus, continuously compressed JPEG images can be regarded as a continuous frame to obtain the convergence error. As the color image converges, its ability to resist interference decreases. To reflect the changes in anti-interference ability, the transposition operation is used to disturb the color JPEG image to obtain the transposition error. In addition, quaternion mapping is used to retain the relationship between continuously compressed JPEG images and enlarge the influence caused by transposition operation. In our experiments on several image databases, the proposed method outperforms existing methods in different settings.

Detection of Double JPEG Compression With the Same Quantization Matrix via Convergence Analysis

Detecting double JPEG compression with the same quantization matrix is a challenging task in image forensics. To address this problem, in this paper, a novel method is proposed by leveraging the component convergence during repeated JPEG compressions. Firstly, an in-depth analysis of the pipeline in successive JPEG compressions is conducted, and it reveals that the rounding/truncation errors as well as JPEG coefficients tend to converge after multiple recompressions. Based on this fact, the backward quantization error (BQE) is defined, and we find that the ratio of non-zero BQE for single compression is larger than that for double compression. Moreover, to exploit the convergence property of JPEG coefficients, a multi-threshold strategy is designed for capturing the statistics of the number of different JPEG coefficients between two sequential compressions. Finally, the statistical features of the dual components are concatenated into a 15-D vector to detect double JPEG compression. Experimental results demonstrate the efficiency of the proposed method, which outperforms some state-of-the-art schemes.

Event‐triggered quantized L2−L∞$$ {\mathfrak{L}}_2-{\mathfrak{L}}_{\infty } $$ filtering for neural networks under denial‐of‐service attacks

AbstractThis article deals with the reliable event‐triggered quantized filtering issue for neural networks with exterior interference under denial‐of‐service attacks. In order to lighten the load of communication channels and save network resources, a resilient event‐triggered mechanism and a quantization scheme are employed, simultaneously. By applying a piecewise Lyapunov–Krasovskii functional method, sufficient conditions containing limitations of denial‐of‐service attacks are derived to guarantee that the filter error system is exponentially stable as well as possesses a prescribed disturbance attenuation performance. Then, a co‐design method of the desired quantized filtering gain matrix and event‐triggering parameter can be obtained provided that the linear matrix inequalities have a feasible solution. Finally, the usefulness of the proposed design method is demonstrated by a numerical example.

High-Efficiency Video Coder in Pruned Environment Using Adaptive Quantization Parameter Selection

The high-efficiency video coder (HEVC) is one of the most advanced techniques used in growing real-time multimedia applications today. However, they require large bandwidth for transmission through bandwidth, and bandwidth varies with different video sequences/formats. This paper proposes an adaptive information-based variable quantization matrix (AI-VQM) developed for different video formats having variable energy levels. The quantization method is adapted based on video sequence using statistical analysis, improving bit budget, quality and complexity reduction. Further, to have precise control over bit rate and quality, a multi-constraint prune algorithm is proposed in the second stage of the AI-VQM technique for pre-calculating K numbers of paths. The same should be handy to self-adapt and choose one of the K-path automatically in dynamically changing bandwidth availability as per requirement after extensive testing of the proposed algorithm in the multi-constraint environment for multiple paths and evaluating the performance based on peak signal to noise ratio (PSNR), bit-budget and time complexity for different videos a noticeable improvement in rate-distortion (RD) performance is achieved. Using the proposed AI-VQM technique, more feasible and efficient video sequences are achieved with less loss in PSNR than the variable quantization method (VQM) algorithm with approximately a rise of 10%–20% based on different video sequences/formats

Double image encryption algorithm based on compressive sensing and elliptic curve

A new improved three-dimensional continuous chaotic system (ImproBsys) is designed in this paper. It can achieve from an ordinary chaotic state to a hyperchaotic state, that is, chaotic behavior tends to become more complex. Furthermore, by using ImproBsys, this paper proposes a double image encryption algorithm based on compressive sensing and public key elliptic curve. First, the two plain images of the same size are excuted by discrete wavelet transformation (DWT), and then the DWT coefficients are thresholded. Second, the quantization matrix is compressed by compressive sensing, and the size is reduced to half of the original one, and then the two compressed matrices are spliced together to form a new matrix. Finally, the new matrix is encrypted by elliptic curve cipher to get the cipher image. Our contributions are: (1) A new ImproBsys is designed with better chaotic behavior, which has two positive Lyapunov exponents to show hyperchaos phenomenon. (2) Compressive sensing technique is employed to reduce the amount of data transmission for two single images, and then we use ImproBsys to control the measurement matrix. (3) The initial values of the ImproBsys depend on the information entropy of the plain image by a new constructed mathematical model.

Spectrum and energy efficient kalman‐based hybrid combiner for mmWave massive MIMO systems

The hybrid beamforming can significantly improve the spectral and energy efficiencies of the millimeter-wave (mmWave) massive multiple input multiple output (MIMO) systems without obvious performance loss. However, the fundamental limitation of existing hybrid beamforming is that they include high-resolution analog-to-digital converters (ADCs) which consume high power. To overcome this fundamental limitation, in this paper, spectrum and energy efficient mmWave massive MIMO transmission scheme is proposed that jointly optimizes the hybrid combiner and the number of ADC bits. Particularly, the hybrid combiner is presented as a combination of three parts: the analog combiner matrix, the quantization matrix, and the digital baseband matrix which is based on the Kalman filter. Unlike most existing works that rely on sumrate optimization to design the hybrid combiner, in this work, the mean square error (MSE) is chosen as the main performance metric to describe the reliability of the system. Simulations results confirm that the proposed Kalman based hybrid combiner exhibits the best performance in terms of spectral and energy efficiencies compared to minimum mean square error (MMSE) and zero forcing (ZF) receivers.

Deep dual-domain semi-blind network for compressed image quality enhancement

Compressed image quality enhancement has attracted a large amount of attention in recent years. In general, the primary goal of compression is artifact reduction to produce a higher-quality output from a low-quality input. Information loss and compression artifacts are mostly due to quantization. The quantization matrix is determined by the compression quality factor (QF). However, there has thus far been little related research to estimate the compression quality factor for JPEG images. To address this issue, in this paper, we propose a deep dual-domain semi-blind network (D3SN) that combines compression quality factor detection and compressed image quality enhancement. Specifically, a quality factor detection (QFD) module is designed to capture contextual information of the space and frequency domains. Furthermore, we build a novel deep dual-domain compressed image quality enhancement network to remove the compression artifacts by using the prior in terms of both the discrete cosine transform (DCT) and pixel domains. Different from previous algorithms, our proposed approach can remove compression artifacts generated at different quality factors by inferring the image quality. Experimental results demonstrate the superiority of our deep dual-domain semi-blind network over state-of-the-art methods in terms of objective quality and visual results.

Intelligent Recognition Method of Athlete Wrong Movement Based on Image Vision

Current methods of human body movement recognition neglect the depth denoising and edge restoration of movement image, which leads to great error in athletes’ wrong movement recognition and poor application intelligence. Therefore, an intelligent recognition method based on image vision for sports athletes’ wrong actions is proposed. The basic principle, structure, and 3D application of computer image vision technology are defined. Capturing the human body image and point cloud data, the three-dimensional dynamic model of sports athletes action is constructed. The color camera including CCD sensor and CMOS sensor is selected to collect the wrong movement image of athlete and provide image data for the recognition of wrong movement. Wavelet transform coefficient and quantization matrix threshold are introduced to denoise the wrong motion images of athletes. Based on this, the feature of sports athlete’s motion contour image is extracted in spatial frequency domain, and the edge of the image is further recovered by Canny operator. Experimental results show that the proposed method can accurately identify the wrong movements of athletes, and there is no redundancy in the recognition results. Image denoising effect is good and less time-consuming and can provide a reliable basis for related fields.

Boosting CNN-based primary quantization matrix estimation of double JPEG images via a classification-like architecture

Estimating the primary quantization matrix of double JPEG compressed images is a problem of relevant importance in image forensics since it allows to infer important information about the past history of an image. In addition, the inconsistencies of the primary quantization matrices across different image regions can be used to localize splicing in double JPEG tampered images. Traditional model-based approaches work under specific assumptions on the relationship between the first and second compression qualities and on the alignment of the JPEG grid. Recently, a deep learning-based estimator capable to work under a wide variety of conditions has been proposed that outperforms tailored existing methods in most of the cases. The method is based on a convolutional neural network (CNN) that is trained to solve the estimation as a standard regression problem. By exploiting the integer nature of the quantization coefficients, in this paper, we propose a deep learning technique that performs the estimation by resorting to a simil-classification architecture. The CNN is trained with a loss function that takes into account both the accuracy and the mean square error (MSE) of the estimation. Results confirm the superior performance of the proposed technique, compared to the state-of-the art methods based on statistical analysis and, in particular, deep learning regression. Moreover, the capability of the method to work under general operative conditions, regarding the alignment of the second compression grid with the one of first compression and the combinations of the JPEG qualities of former and second compression, is very relevant in practical applications, where these information are unknown a priori.