Compression Distortion Research Articles

INTERVAL QUALITY INDICATORS OF THE DYNAMIC RANGE COMPRESSION OF INFRARED IMAGES ON THE BASIS OF A TONE MAPPING MATRIX

The article proposes a dynamic range compression model for infrared (IR) images based on a tone mapping
 matrix, the elements of which relate the brightness levels of the original image with a wide dynamic range and
 the brightness levels of a non-linearly transformed image with a narrow dynamic range, and also indicate, depending
 on the variant of formation of this matrices on: a) loss of discrimination between adjacent pixels due
 to compression of the dynamic range; b) the level of non-linear compression distortions; c) ambiguity of tone
 mapping. Based on this model, interval indicators of the quality of compression of the dynamic range of infrared
 images are proposed, which allow estimating the potential distinguishing power, the real loss of discrimination
 between adjacent pixels after transformation, the magnitude of nonlinear compression distortions, the uniformity
 of the use of the dynamic range, and the ambiguity of tone mapping for the selected interval of the dynamic range.
 The proposed indicators improve the accuracy of assessing the quality of compression of the dynamic range of IR
 images by expanding the system of known indicators that evaluate the contrast, entropy, statistical naturalness
 of the converted images, and the structural accuracy of tone mapping.

Evaluation of lossy compression algorithms using discrete cosine transform for sounding rocket vibration data

This work evaluates the performance of compression algorithms based on discrete cosine transform (DCT) when used to encode sounding rockets’ vibration signals. Different compression methods that use the three-step strategy (transform, quantization, and entropy encoder) are presented. The performances are evaluated using the relationship between the compression rate and distortion, the latter being measured using the peak signal-to-noise ratio (PSNR) and the difference observed in the power spectral density. The focus of the work is on vibration signals from rocket launches conducted by the Institute of Aeronautics and Space of the Brazilian Air Force. The obtained results present a great variation for the vibration data in the test set. Compressors operating with PSNR close to 50 dB can achieve very high compression rates (over 100) for low power signals. However, if the vibration signal strength increases significantly, the compression rate can be much lower. For the worst case in the test set (signal with the highest power), the work shows that the best proposed method needs a rate of 9.8 to achieve a PSNR of 48.2 dB. When compared to the performance of recently published DCT-based compression algorithms, the proposed technique shows significant improvements. Given a PSNR around 50 dB, for signal with moderate power, improvements above 100% are observed in compression rate.

THz gain compression in nanoscale FinFETs

The gain compression and excitation of higher order harmonics in 3 nm wide on-insulator type FinFETs is investigated. The high computational burden related to the time-resolved analysis of such three-dimensional devices is dealt with by applying a mode-space approach onto a Quantum Liouville-type Equation. With this approach an increase in gain compression and higher order harmonic distortion is observed when decreasing the fin height from 5 to 3 nm.

Perceptually Quasi-Lossless Compression of Screen Content Data Via Visibility Modeling and Deep Forecasting

Screen content data, such as computer-generated photographs, desktop sharing, remote education, video game streaming and screenshot, is one of the most popular visual information carriers in Internet of Video Things. Although lossless compression can guarantee high quality of service for these screen content based industrial applications, it also causes considerable storage space and transmission bandwidth issues. To alleviate these challenges, in this article, we present a visually quasi-lossless coding approach to control the compression distortion below <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">visibility threshold</i> in the human visual system. Specifically, to better quantify the visual redundancy for screen content data, a new <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">visibility threshold</i> method is designed by incorporating blur sensitivity and oblique correction effects. Then, an end-to-end mapping between the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">visibility threshold</i> and quality control factor is learned and represented as a deep convolutional neural network. The experimental results demonstrate that the proposed method saves the average encoding bits up to 23.15% compared with the latest scheme under the same perceptual quality.

RDEN: Residual Distillation Enhanced Network-Guided Lightweight Synthesized View Quality Enhancement for 3D-HEVC

In the three-dimensional video system, the depth image-based rendering is a key technique for generating synthesized views, which provides audiences with depth perception and interactivity. However, the inaccuracy of depth information leads to geometrical rendering position errors, and the compression distortion of texture and depth videos degrades the quality of the synthesized views. Although existing quality enhancement methods can eliminate the distortions in the synthesized views, their huge computational complexity hinders their applications in real-time multimedia systems. To this end, a residual distillation enhanced network (RDEN)-guided lightweight synthesized view quality enhancement (SVQE) method is proposed to minimize holes and compression distortions in the synthesized views while reducing the model complexity. First, a rethinking on the deep-learning-based SVQE methods is performed. Then, a feature distillation attention block is proposed to effectively reduce the distortions in the synthesized views and make the model fulfill more real-time tasks, which is a lightweight and flexible feature extraction block using an information distillation mechanism and a lightweight multi-scale spatial attention mechanism. Third, a residual feature fusion block is proposed to improve the enhancement performance by using the feature fusion mechanism, which efficiently improves the feature extraction capability without introducing any additional parameters. Experimental results prove that the proposed RDEN efficiently improves the SVQE performance while consuming few computational complexities compared with the state-of-the-art SVQE methods.

Robust video steganography framework based on channel-space attention

In robust video steganography, a message is embedded into a video such that video distortions are avoided while producing a stego video of imperceptible difference from the cover video. Traditional techniques achieved robustness against particular distortions but are complicated in computation and design, and rely on different compression standards. Nowadays, deep-learning-based methods can achieve impressive visual quality and robustness to attacks. We propose a framework with a channel-space attention mechanism for robust video steganography. The framework is composed of depthwise separable convolution layers that can learn channel-space segments for embedding and extraction. The secret messages are distributed across channel-space scales to increase imperceptibility and robustness to distortions. This end-to-end solution is trained with the 3-player game approach to conducting robust steganography, where three networks compete. Two of these handle embedding and extraction operations, while the third network simulates attacks and detection from a steganalyst as an adversarial network. Comparative results versus recent research show that our method is more robust against compression and video distortion attacks. Peak signal-to-noise ratio and structural similarity index were used for evaluating visual quality and demonstrate the imperceptibility of our method.

On the impact of VR assessment on the quality of experience of highly realistic digital humans

Fuelled by the increase in popularity of virtual and augmented reality applications, point clouds have emerged as a popular 3D format for acquisition and rendering of digital humans, thanks to their versatility and real-time capabilities. Due to technological constraints and real-time rendering limitations, however, the visual quality of dynamic point cloud contents is seldom evaluated using virtual and augmented reality devices, instead relying on prerecorded videos displayed on conventional 2D screens. In this study, we evaluate how the visual quality of point clouds representing digital humans is affected by compression distortions. In particular, we compare three different viewing conditions based on the degrees of freedom that are granted to the viewer: passive viewing (2DTV), head rotation (3DoF), and rotation and translation (6DoF), to understand how interacting in the virtual space affects the perception of quality. We provide both quantitative and qualitative results of our evaluation involving 78 participants, and we make the data publicly available. To the best of our knowledge, this is the first study evaluating the quality of dynamic point clouds in virtual reality, and comparing it to traditional viewing settings. Results highlight the dependency of visual quality on the content under test, and limitations in the way current data sets are used to evaluate compression solutions. Moreover, influencing factors in quality evaluation in VR, and shortcomings in how point cloud encoding solutions handle visually-lossless compression, are discussed.

On the trade-off between compression efficiency and distortion of a new compression algorithm for multichannel EEG signals based on singular value decomposition

In this article we investigate the trade-off between the compression ratio and distortion of a recently published compression technique specifically devised for multichannel electroencephalograph (EEG) signals. In our previous paper, we proved that, when singular value decomposition (SVD) is already performed for denoising or removing unwanted artifacts, it is possible to exploit the same SVD for compression purpose by achieving a compression ratio in the order of 10 and a percentage root mean square distortion in the order of 0.01 %. In this article, we successfully demonstrate how, with a negligible increase in the computational cost of the algorithm, it is possible to further improve the compression ratio by about 10 % by maintaining the same distortion level or, alternatively, to improve the compression ratio by about 50 % by still maintaining the distortion level below the 0.1 %.

A Novel Reduced Reference Image Quality Assessment Based on Formal Concept Analysis

Abstract The assessment of image quality provides the confidentiality, safety, quality and transparency of the obtained images. At the same time, there are many existing quality metric approaches that belong to the group of modification alteration procedures (pixel-based metric). These techniques have not been well-matched with perceptual image quality. The Perceptual Human Visual System (HVS) aspect drives our approach; The human visual system (HVS) is the best judge of image quality. This paper presents a new reduced reference image quality metric in the spatial domain that reduces the complexity of the image quality assessment. From the reference and distortion images, we extract four intrinsic features, namely, contrast, entropy, histogram and standard deviation. Then, we build the formal concept analysis matrix for reference and distortion images. Finally, we compare the obtained matrixes to evaluate the image quality. The performance of the proposed technique is assessed using LIVE, TID2013 and CSIQ datasets, and the obtained results are compared in terms of PSNR, SSIM and NCC metrics. Also, a comparison with more recent and relevant approaches is performed to highlight the superior performance of our proposed approach, the experimental results indicated that the proposed approach provides efficient performance among the compression, Gaussian blur, Contrast and add noise distortion types.

TGP-PCQA: Texture and geometry projection based quality assessment for colored point clouds

Colored point cloud (PC) will inevitably encounter distortion during its acquisition, processing, coding and transmission, which may affect the visual quality of the colored PC. Therefore, it is necessary to design an effective tool for colored PC quality assessment (PCQA). In this paper, considering the mapping relationship of perception between the colored PC and its corresponding projection images, we propose a novel PCQA method based on texture and geometry projection (denoted as TGP-PCQA). The main idea of the proposed TGP-PCQA method is to obtain texture and geometry projection maps from different perspectives for evaluating the colored PC. Specifically, 4D tensor decomposition is used to obtain the combination and difference information between the reference and distorted texture projection maps for mainly characterizing texture distortion of colored PC. Meanwhile, the edge features of the geometry projection map are calculated to measure the global or local geometry distortion. All of the extracted features are combined to predict an overall quality of colored PC. In addition, this paper establishes a multi-distorted colored PC database named CPCD2.0 with compression distortions and Gaussian noise, which orients to the influence of both geometry and texture components in distortion. Experimental results on two open subjective evaluation databases (IRPC and SJTU-PCQA) and the self-built CPCD2.0 database show that the proposed TGP-PCQA method outperforms the state-of-the-art PCQA methods. We are also providing the self-built CPCD2.0 database free of charge at https://github.com/cherry0415/CPCD2.0.

Expert training: Enhancing AI resilience to image coding artifacts

In the Machine-to-Machine (M2M) transmission context, there is a great need to reduce the amount of transmitted information using lossy compression. However, commonly used image compression methods are designed for human perception, not for Artificial Intelligence (AI) algorithms performances. It is known that these compression distortions affect many deep learning based architectures on several computer vision tasks. In this paper, we focus on the classification task and propose a new approach, named expert training, to enhance Convolutional Neural Networks (CNNs) resilience to compression distortions. We validated our approach using MnasNet and ResNet50 architectures, against image compression distortions introduced by three commonly used methods (JPEG, J2K and BPG), on the ImageNet dataset. The results showed a better robustness of these two architectures against the tested coding artifacts using the proposed expert training approach. Our code is publicly available at https://github.com/albmarie/expert_training.

Computational studies of the magneto-structural correlations in a manganese dimer with Jahn-Teller distortions.

Inspired by the crystal structure of a MnIII dinuclear complex we obtained featuring both Jahn-Teller (JT) elongation and compression distortions, we have modelled a series of complex cations based on the disordered crystal formulation; [Mn2(L1)2(μ2-OH)2)4+ (1), [Mn2(L1)(L2)(μ2-OH)2)4+ (2), [Mn2(L2)(L1)(μ2-OH)2)4+ (3), and [Mn2(L2)2(μ2-OH)2)4+ (4) (where L1 = (1E,1'E)-5-tert-butyl-3-(((4-(((5-tert-butyl-2-hydroxy-3-((E)-(hydroxyimino)methyl)benzyl)(methyl)amino)methyl)benzyl)(methyl)amino)methyl)-2-hydroxybenzaldehyde and L2 = 3,3'-(1,4-phenylenebis(methylene))bis(methylazanediyl)bis(methylene)bis(5-tert-butyl-2-hydroxybenzaldehyde)) with different geometries to investigate the effects of the distortions on the magnetic coupling parameter. All computationally modelled dimers had a ferromagnetic interaction between the MnIII centres, with greater magnetic coupling calculated for complexes with both JT elongation and compression present. The ferromagnetic contribution to the J coupling was ascribed to the orthogonality of the singly occupied magnetic orbitals along with the cross-interaction between the unfilled Mn1(dx2-y2) and singly occupied Mn2(dx2-y2) orbitals. Constrained calculations showed that reducing the extent of the compression at Mn2 results in a concomitant increase in the dihedral angle between the JT axes, thereby reducing the relative magnitude of the magnetic coupling between MnIII centres.

Quality Evaluation of Holographic Images Coded With Standard Codecs

Recently, a larger 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 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 compression methodology for digital holograms 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 pseudo-videos 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.

Subjective Quality Database and Objective Study of Compressed Point Clouds With 6DoF Head-Mounted Display

In this paper, we focus on subjective and objective Point Cloud Quality Assessment (PCQA) in an immersive environment and study the effect of geometry and texture attributes in compression distortion. Using a Head-Mounted Display (HMD) with six degrees of freedom, we establish a subjective PCQA database, named SIAT Point Cloud Quality Database (SIAT-PCQD). Our database consists of 340 distorted point clouds compressed by the MPEG point cloud encoder with the combination of 20 sequences and 17 pairs of geometry and texture quantization parameters. The impact of distorted geometry and texture attributes is further discussed in this paper. Then, we propose two projection-based objective quality evaluation methods, i.e., a weighted view projection based model and a patch projection based model. Our subjective database and findings can be used in point cloud processing, transmission, and coding, especially for virtual reality applications. The subjective dataset has been released in the public repository.

Relation between hearing abilities and preferred playback settings for speech perception in complex listening conditions

Objective This study investigated if individual preferences with respect to the trade-off between a good signal-to-noise ratio and a distortion-free speech target were stable across different masking conditions and if simple adjustment methods could be used to identify subjects as either “noise haters” or “distortions haters”. Design In each masking condition, subjects could adjust the target speech level according to their preferences by employing (i) linear gain or gain at the cost of (ii) clipping distortions or (iii) compression distortions. The comparison of these processing conditions allowed investigating the preferred trade-off between distortions and noise disturbance. Study sample Thirty subjects differing widely in hearing status (normal-hearing to moderately impaired) and age (23–85 years). Results High test–retest stability of individual preferences was found for all modification schemes. The preference adjustments suggested that subjects could be consistently categorised along a scale from “noise haters” to “distortion haters”, and this preference trait remained stable through all maskers, spatial conditions, and types of distortions. Conclusions Employing quick self-adjustment to collect listening preferences in complex listening conditions revealed a stable preference trait along the “noise vs. distortions” tolerance dimension. This could potentially help in fitting modern hearing aid algorithms to the individual user.

Theoretical studies of the EPR spectra and local structures for Cu2+ and V4+ doped halo-borate glasses

Local structure and the effect of halide on MX∙M2O∙B2O3 (M = Pb, Ba; X = F, Cl, Br) glasses doped with Cu2+ and VO2+ are investigated by means of the perturbation calculations of the spin Hamiltonian parameters (SHPs). Based on the fitted EPR results, [CuO6]10− and [VO6]8− clusters prefer to suffer the tetragonal elongation and compression distortions, respectively. Compare the ionic radius of Cu2+ with V4+ and Pb2+ with Ba2+ in MX∙M2O∙B2O3, the glasses with larger ionic radius of metal may induce larger tetragonal distortions and larger g anisotropies. The d-d transition bands show whole opposite trends for 3d9 and 3d1 impurities with the similar fluctuations due to the increase of non-bridging oxygen. The d-d transition bands and g factorsare are also analyzed from the electronegativities of incorporated halides. The values of basicities Λth of BCBBV are found larger than PCPBC and PbX-PbO-B2O3 glasses due to stronger ionicity of Ba than Pb.

Monitoring Real-Time Status of Analog Sources: A Cross-Layer Approach

Real-time status updating or monitoring plays a critical role in emerging applications including Industrial Internet of Things (IIoT) and Vehicular-to-Everything (V2X) systems. In these applications, the real-time status is mostly characterized by analog signal samples desiring lossy compression before digital transmissions. However, how to ensure the data freshness while reducing the distortion due to lossy compression still remains open. In this paper, we are interested in a cross-layer framework aiming at achieving low Age-of-Information (AoI) and compression distortion concurrently in real-time monitoring over fading channels. More specifically, a cross-layer optimization is formulated to jointly control the lossy compression in the application layer and data transmission in the physical layer. We present a hierarchical solution method by decomposing the cross-layer optimization into inner and outer problems. The objective of the inner problem, solved by convex optimization, is to minimize an age-weighted distortion function and strike the optimal tradeoff between the instantaneous AoI and compression loss. The objective of the outer problem, solved by dimension-reduced Constrained Markov Decision Process (CMDP), is to acquire the optimal scheduling policy reducing the average AoI and distortion. We demonstrate the structural results of the optimal cross-layer design, which substantially reduces the protocol complexity in practice.

Sparse Representations-based depth images quality assessment

The conventional 2D metrics can be used for measuring the quality of depth maps, but none of them is considered to be efficient and is not accurate when used for evaluating 3D quality. In this paper, we propose a new full reference objective metric, called Sparse Representations-Mean Squared Error (SR-MSE), which efficiently evaluates the depth maps compression distortions. It adaptively models the reference and compressed depth maps in a mixed redundant transform domain dedicated to depth features. Then, it computes the mean squared error between the sparse coefficients issued from this modeling. As a benchmark of quality assessment, we perform a subjective evaluation test for depth maps compressed using the latest 3D High Efficiency Video Coding standard at various bitrates. We compare the subjective results with the proposed and conventional objective metrics. Experimental results demonstrate that the proposed SR-MSE, compared to the conventional image quality assessment metrics, yields the highest correlated scores to the subjective ones.

TSAN: Synthesized View Quality Enhancement via Two-Stream Attention Network for 3D-HEVC

In three-dimensional video system, the texture and depth videos are jointly encoded, and then the Depth Image Based Rendering (DIBR) is utilized to realize view synthesis. However, the compression distortion of texture and depth videos, as well as the disocclusion problem in DIBR degrade the visual quality of the synthesized view. To address this problem, a Two-stream Attention Network (TSAN)-based synthesized view quality enhancement method is proposed for 3D-High Efficiency Video Coding (3D-HEVC) in this article. First, the shortcomings of the view synthesis technique and traditional convolutional neural networks are analyzed. Then, based on these analyses, a TSAN with two information extraction streams is proposed for enhancing the quality of the synthesized view, in which the global information extraction stream learns the contextual information, and the local information extraction stream extracts the texture information from the rendered image. Third, a Multi-Scale Residual Attention Block (MSRAB) is proposed, which can efficiently detect features in different scales, and adaptively refine features by considering interdependencies among spatial dimensions. Extensive experimental results show that the proposed synthesized view quality enhancement method achieves significantly better performance than the state-of-the-art methods.

Structural-Information-Based Robust Corner Point Extraction for Camera Calibration Under Lens Distortions and Compression Artifacts

Previous camera calibration methods often use a checkerboard to capture images and estimate the camera parameters from the correspondences between images and the checkerboard. The corner points in the checkerboard images are used as useful features for correspondence matching. Therefore, it is essential to precisely find the corner points in the checkerboard images. In many previous works, the corner points are extracted assuming that the checkerboard images are not distorted by its lens. Instead, image blurring and Gaussian noise on the images are usually considered, but other cases are not dealt with. However, the captured checkerboard images are often corrupted by lens distortions and compression artifacts, which leads to performance degradation of corner point extraction. Moreover, the corner points are extracted individually in the previous methods without considering their geometric relations. To better handle the corner point extraction problem under lens distortions, in our corner point extraction optimization, the distorted locations of the pixels on checkerboard images are corrected with the camera parameters, and the structural constraints for checkerboard image grids are then applied under the line-to-line mapping. Also, to robustly find the blurred edges between corner points due to JPEG compression, an edge surface model is newly proposed that models the transitions with over- and under-shoots around the blurred edges. Extensive experimental results show that our method significantly outperforms the state-of-the-art method with average 88.3% and 54.3% reduction in RMSE for corner point reprojection and camera parameter estimation, respectively under compression and lens distortions for synthetic and real data.