nils-herrmann/clean_scirepeval huggingface dataset:
The Role of Spatial Information in Disentangling the Irradiance–Reflectance–Transmittance Ambiguity

In the satellite hyperspectral measures, the contributions of light, surface, and atmosphere are mixed. Applications need separate access to the sources. Conventional inversion techniques usually take a pixelwise spectral-only approach. However, recent improvements in retrieving surface and atmosphere characteristics use heuristic spatial smoothness constraints. In this paper, we theoretically justify such heuristics by analyzing the impact of spatial information on the uncertainty of the solution. The proposed analysis allows to assess in advance the uniqueness (or robustness) of the solution depending on the curvature of a likelihood surface. In situations where pixel-based approaches become unreliable, it turns out that the consideration of spatial information always makes the problem to be better conditioned. With the proposed analysis, this is easily understood since the curvature is consistent with the complexity of the sources measured in terms of the number of significant eigenvalues (or free parameters in the problem). In agreement with recent results in hyperspectral image coding, spatial correlations in the sources imply that the intrinsic complexity of the spatio-spectral representation of the signal is always lower than its spectral-only counterpart. According to this, the number of free parameters in the spatio-spectral inverse problem is smaller, so the spatio-spectral approaches are always better than spectral-only approaches. Experiments using ensembles of actual reflectance values and realistic MODTRAN irradiance and atmosphere radiance and transmittance values show that the proposed analysis successfully predicts the practical difficulty of the problem and the improved quality of spatio-spectral retrieval.
Perceptually and statistically decorrelated features for image representation: application to transform coding

Transform coding consists of a scalar quantization of the features of an image representation. These features should be independent enough to justify the scalar approach. The coefficients of the commonly used DCT representation still show some dependence that may reduce its efficiency. In this work, a perceptually inspired nonlinear transform is used to map the DCT into a new representation that largely reduces the statistical and perceptual relations between the coefficients thus improving the compression performance.
Regularization operators for natural images based on nonlinear perception models

Image restoration requires some a priori knowledge of the solution. Some of the conventional regularization techniques are based on the estimation of the power spectrum density. Simple statistical models for spectral estimation just take into account second-order relations between the pixels of the image. However, natural images exhibit additional features, such as particular relationships between local Fourier or wavelet transform coefficients. Biological visual systems have evolved to capture these relations. We propose the use of this biological behavior to build regularization operators as an alternative to simple statistical models. The results suggest that if the penalty operator takes these additional features in natural images into account, it will be more robust and the choice of the regularization parameter is less critical.
Iterative Gaussianization: From ICA to Random Rotations

Most signal processing problems involve the challenging task of multidimensional probability density function (PDF) estimation. In this paper, we propose a solution to this problem by using a family of rotation-based iterative Gaussianization (RBIG) transforms. The general framework consists of the sequential application of a univariate marginal Gaussianization transform followed by an orthonormal transform. The proposed procedure looks for differentiable transforms to a known PDF so that the unknown PDF can be estimated at any point of the original domain. In particular, we aim at a zero-mean unit-covariance Gaussian for convenience. RBIG is formally similar to classical iterative projection pursuit algorithms. However, we show that, unlike in PP methods, the particular class of rotations used has no special qualitative relevance in this context, since looking for interestingness is not a critical issue for PDF estimation. The key difference is that our approach focuses on the univariate part (marginal Gaussianization) of the problem rather than on the multivariate part (rotation). This difference implies that one may select the most convenient rotation suited to each practical application. The differentiability, invertibility, and convergence of RBIG are theoretically and experimentally analyzed. Relation to other methods, such as radial Gaussianization, one-class support vector domain description, and deep neural networks is also pointed out. The practical performance of RBIG is successfully illustrated in a number of multidimensional problems such as image synthesis, classification, denoising, and multi-information estimation.
Nonlinearities and Adaptation of Color Vision from Sequential Principal Curves Analysis

Mechanisms of human color vision are characterized by two phenomenological aspects: the system is nonlinear and adaptive to changing environments. Conventional attempts to derive these features from statistics use separate arguments for each aspect. The few statistical explanations that do consider both phenomena simultaneously follow parametric formulations based on empirical models. Therefore, it may be argued that the behavior does not come directly from the color statistics but from the convenient functional form adopted. In addition, many times the whole statistical analysis is based on simplified databases that disregard relevant physical effects in the input signal, as, for instance, by assuming flat Lambertian surfaces. In this work, we address the simultaneous statistical explanation of the nonlinear behavior of achromatic and chromatic mechanisms in a fixed adaptation state and the change of such behavior (i.e., adaptation) under the change of observation conditions. Both phenomena emerge directly from the samples through a single data-driven method: the sequential principal curves analysis (SPCA) with local metric. SPCA is a new manifold learning technique to derive a set of sensors adapted to the manifold using different optimality criteria. Here sequential refers to the fact that sensors (curvilinear dimensions) are designed one after the other, and not to the particular (eventually iterative) method to draw a single principal curve. Moreover, in order to reproduce the empirical adaptation reported under D65 and A illuminations, a new database of colorimetrically calibrated images of natural objects under these illuminants was gathered, thus overcoming the limitations of available databases. The results obtained by applying SPCA show that the psychophysical behavior on color discrimination thresholds, discount of the illuminant, and corresponding pairs in asymmetric color matching emerge directly from realistic data regularities, assuming no a priori functional form. These results provide stronger evidence for the hypothesis of a statistically driven organization of color sensors. Moreover, the obtained results suggest that the nonuniform resolution of color sensors at this low abstraction level may be guided by an error-minimization strategy rather than by an information-maximization goal.
Perceptually weighted optical flow for motion-based segmentation in MPEG-4 paradigm

In the MPEG-4 paradigm, the sequence must be described in terms of meaningful objects. This meaningful, high-level representation should emerge from low-level primitives such as optical flow and prediction error which are the basic elements of previous-generation video coders. The accuracy of the high-level models strongly depends on the robustness of the primitives used. It is shown how perceptual weighting in optical flow computation gives rise to better motion estimates which consistently improve motion-based segmentation compared to equivalent unweighted motion estimates.
Perceptual feedback in multigrid motion estimation using an improved DCT quantization

In this paper, a multigrid motion compensation video coder based on the current human visual system (HVS) contrast discrimination models is proposed. A novel procedure for the encoding of the prediction errors has been used. This procedure restricts the maximum perceptual distortion in each transform coefficient. This subjective redundancy removal procedure includes the amplitude nonlinearities and some temporal features of human perception. A perceptually weighted control of the adaptive motion estimation algorithm has also been derived from this model. Perceptual feedback in motion estimation ensures a perceptual balance between the motion estimation effort and the redundancy removal process. The results show that this feedback induces a scale-dependent refinement strategy that gives rise to more robust and meaningful motion estimation, which may facilitate higher level sequence interpretation. Perceptually meaningful distortion measures and the reconstructed frames show the subjective improvements of the proposed scheme versus an H.263 scheme with unweighted motion estimation and MPEG-like quantization.
Importance of quantiser design compared to optimal multigrid motion estimation in video coding

Adaptive flow computation and DCT quantisation play complementary roles in motion compensated video coding schemes. Since the introduction of the intuitive entropy-constrained motion estimation of Dufaux et al. (1995), several optimal variable-size block matching algorithms have been proposed. Many of these approaches put forward their intrinsic optimality, but the corresponding visual effect has not been explored. The relative importance of optimal multigrid motion estimation with regard to quantisation is addressed in the context of MPEG-like coding. It is shown that while simpler (suboptimal) motion estimates give subjective results as good as the optimal motion estimates, small enhancements in the quantiser have significant visual effects. This suggests that more attention should be paid to the quantiser design.
Perceptual adaptive insensitivity for support vector machine image coding

Support vector machine (SVM) learning has been recently proposed for image compression in the frequency domain using a constant /spl epsiv/-insensitivity zone by Robinson and Kecman. However, according to the statistical properties of natural images and the properties of human perception, a constant insensitivity makes sense in the spatial domain but it is certainly not a good option in a frequency domain. In fact, in their approach, they made a fixed low-pass assumption as the number of discrete cosine transform (DCT) coefficients to be used in the training was limited. This paper extends the work of Robinson and Kecman by proposing the use of adaptive insensitivity SVMs for image coding using an appropriate distortion criterion , based on a simple visual cortex model. Training the SVM by using an accurate perception model avoids any a priori assumption and improves the rate-distortion performance of the original approach.
Graph Matching for Adaptation in Remote Sensing

We present an adaptation algorithm focused on the description of the data changes under different acquisition conditions. When considering a source and a destination domain, the adaptation is carried out by transforming one data set to the other using an appropriate nonlinear deformation. The eventually nonlinear transform is based on vector quantization and graph matching. The transfer learning mapping is defined in an unsupervised manner. Once this mapping has been defined, the samples in one domain are projected onto the other, thus allowing the application of any classifier or regressor in the transformed domain. Experiments on challenging remote sensing scenarios, such as multitemporal very high resolution image classification and angular effects compensation, show the validity of the proposed method to match-related domains and enhance the application of cross-domains image processing techniques.
Video quality measures based on the standard spatial observer

Video quality metrics are intended to replace human evaluation with evaluation by machine. To accurately simulate human judgement, they must include some aspects of the human visual system. In this paper we present a class of low-complexity video quality metrics based on the standard spatial observer (SSO). In these metrics, the basic SSO model is improved with several additional features from the current human vision models. To evaluate the metrics, we make use of the data set recently produced by the Video Quality Experts Group (VQEG), which consists of subjective ratings of 160 samples of digital video covering a wide range of quality. For each metric we examine the correlation between its predictions and the subjective ratings. The results show that SSO-based models with local masking obtain the same degree of accuracy as the best metric considered by VQEG (P5), and significantly better correlations than the other VQEG models. The results suggest that local masking is a key feature to improve the correlation of the basic SSO model.
Normalized image representation for efficient coding

In this paper we propose an adaptive nonlinear image representation based on the divisive normalization of local-frequency transforms used in contrast masking models. This normalized representation has two effects: (1) it increases the statistical independence of the coefficients of the representation and (2) it is Euclidean from a perceptual point of view. Experimental results show that reducing the remaining statistical and perceptual dependence using normalized representations for transform coding may make a big difference in the qualify of the reconstructed images.
Nonlinear image representation for efficient perceptual coding

Image compression systems commonly operate by transforming the input signal into a new representation whose elements are independently quantized. The success of such a system depends on two properties of the representation. First, the coding rate is minimized only if the elements of the representation are statistically independent. Second, the perceived coding distortion is minimized only if the errors in a reconstructed image arising from quantization of the different elements of the representation are perceptually independent. We argue that linear transforms cannot achieve either of these goals and propose, instead, an adaptive nonlinear image representation in which each coefficient of a linear transform is divided by a weighted sum of coefficient amplitudes in a generalized neighborhood. We then show that the divisive operation greatly reduces both the statistical and the perceptual redundancy amongst representation elements. We develop an efficient method of inverting this transformation, and we demonstrate through simulations that the dual reduction in dependency can greatly improve the visual quality of compressed images.
Psychophysically Tuned Divisive Normalization Approximately Factorizes the PDF of Natural Images

The conventional approach in computational neuroscience in favor of the efficient coding hypothesis goes from image statistics to perception. It has been argued that the behavior of the early stages of biological visual processing (e.g., spatial frequency analyzers and their nonlinearities) may be obtained from image samples and the efficient coding hypothesis using no psychophysical or physiological information. In this work we address the same issue in the opposite direction: from perception to image statistics. We show that psychophysically fitted image representation in V1 has appealing statistical properties, for example, approximate PDF factorization and substantial mutual information reduction, even though no statistical information is used to fit the V1 model. These results are complementary evidence in favor of the efficient coding hypothesis.
PCA Gaussianization for image processing

The estimation of high-dimensional probability density functions (PDFs) is not an easy task for many image processing applications. The linear models assumed by widely used transforms are often quite restrictive to describe the PDF of natural images. In fact, additional non-linear processing is needed to overcome the limitations of the model. On the contrary, the class of techniques collectively known as projection pursuit, which solve the high-dimensional problem by sequential univariate solutions, may be applied to very general PDFs (e.g. iterative Gaussianization procedures). However, the associated computational cost has prevented their extensive use in image processing. In this work, we propose a fast alternative to iterative Gaussianization methods that makes it suitable for image processing while ensuring its theoretical convergence. Method performance is successfully illustrated in image synthesis and classification problems.
Comparison of perceptually uniform quantisation with average error minimisation in image transform coding

An alternative transform coder design criterion based on restricting the maximum perceptual error of each coefficient is proposed. This perceptually uniform quantisation of the transform domain ensures that the perceptual error will be below a certain limit regardless of the particular input image. The results show that the proposed criterion improves the subjective quality of the conventional average error criterion even if it is weighted with the same perceptual metric.
Efficient adaptive prediction based reversible image watermarking

In this paper, we propose a new reversible watermarking algorithm based on additive prediction-error expansion which can recover original image after extracting the hidden data. Embedding capacity of such algorithms depend on the prediction accuracy of the predictor. We observed that the performance of a predictor based on full context prediction is preciser as compared to that of partial context prediction. In view of this observation, we propose an efficient adaptive prediction (EAP) method based on full context, that exploits local characteristics of neighboring pixels much effectively than other prediction methods reported in literature. Experimental results demonstrate that the proposed algorithm has a better embedding capacity and also gives better Peak Signal to Noise Ratio (PSNR) as compared to state-of-the-art reversible watermarking schemes.
Multiple sub-pixel interpolation filters with adaptive symmetry for high-resolution video coding

In order to further improve video coding efficiency, a novel adaptive sub-pixel interpolation filter is presented in this paper. Considering the local image characteristics, the proposed method designs interpolation filters for sub-pixels in low-frequent and high-frequent areas separately. And in order to reduce the header information, flexible symmetry is assumed for each filter. Experimental results show that this method achieves up to 0.39dB coding gain which equals to a 11.39% average bit-rate reduction for high-resolution video materials compared to the standard non-adaptive interpolation method of H.264. Compared to the state-of-art 2D non-separable adaptive interpolation scheme, an average bit-rate saving of 0.83% is achieved for high-definition video coding.
Rate Control Based on Zero-Residue Pre-Selection for Video Transcoding

A common issue in video transcoding for heterogeneous network environment is to efficiently and accurately reduce the bit-rate such that the distortion is minimized under a given rate constraint. To convert the bit-rate of an encoded video to match the channel capacity, in general, re-quantization is done on the DCT coefficients with larger quantization step size. Most existing rate control algorithms for video transcoding in the literature calculate quantization parameters (QPs) of macroblocks (MBs) based on a relationship between certain properties of coded video and bit-rate. They reduce the computational complexity by simplifying the R-D model and reusing the statistics information of input video. In this paper, we propose a zero-residue pre-selection (ZRPS) mechanism to select only a portion of MBs to apply the rate control in video transcoding. TMN-8 is used to evaluate the impact of ZRPS. Experimental results show that, as compared to the original TMN-8 rate control scheme, TMN-8 with ZRPS achieves up to 1.60 dB gain, in term of PSNR, and requires less than 50% of the computational complexity compared to TMN-8, depending on the characteristics of the video content
Modified motion compensated temporal frame interpolation for very low bit rate video

Common techniques such as frame repetition or linear interpolation for reconstructing skipped frames in temporally subsampled video sequences tend to introduce undesirable artifacts. A previously proposed technique, fast motion compensated temporal interpolation (FMCTI), can interpolate video frames in the time domain with good interpolated image quality. However, the reconstructed frames tend to be blocky. We propose to incorporate a pixel-based motion classifier into FMCTI to solve the problem. The resulting technique, modified fast motion compensated temporal interpolation (MFMCTI), was found by simulation to give significantly reduced blocking artifacts.
Secure Reversible Image Data Hiding Over Encrypted Domain via Key Modulation

This paper proposes a novel reversible image data hiding scheme over encrypted domain. Data embedding is achieved through a public key modulation mechanism, in which access to the secret encryption key is not needed. At the decoder side, a powerful two-class SVM classifier is designed to distinguish encrypted and nonencrypted image patches, allowing us to jointly decode the embedded message and the original image signal. Compared with the state-of-the-art methods, the proposed approach provides higher embedding capacity and is able to perfectly reconstruct the original image as well as the embedded message. Extensive experimental results are provided to validate the superior performance of our scheme.
Nonparametric density estimation on a graph: Learning framework, fast approximation and application in image segmentation

We present a novel framework for tree-structure embedded density estimation and its fast approximation for mode seeking. The proposed method could find diverse applications in computer vision and feature space analysis. Given any undirected, connected and weighted graph, the density function is defined as a joint representation of the feature space and the distance domain on the graph's spanning tree. Since the distance domain of a tree is a constrained one, mode seeking can not be directly achieved by traditional mean shift in both domain. we address this problem by introducing node shifting with force competition and its fast approximation. Our work is closely related to the previous literature of nonparametric methods. One shall see, however, that the new formulation of this problem can lead to many advantages and new characteristics in its application, as will be illustrated later in this paper.
Analysis of sampling pattern and Luma-Chroma filter design for subpixel-based image downsampling

Subpixel-based image downsampling is attractive in that it produces higher apparent resolution of down-sampled images on LCD displays. However increased luminance resolution is achieved at the price of color fringing artifacts. In this paper, we propose an algorithm to find a pleasing balance between increased resolution and color fidelity. We separate the subpixel-based downsampling into two stages, shifting followed by downsampling with anti-aliasing filtering. In stage one, we find special characteristics of the luminance and chrominance spectra of the shifted image, based on which the optimal sampling pattern is found. In stage two, anti-aliasing filters for luminance and chrominance are designed respectively. Experimental results verify that the proposed method manages to suppress color artifacts while maintaining high luminance sharpness.