nils-herrmann/clean_scirepeval huggingface dataset:
Cross-view post-filtering for fidelity enhancement on asymmetric coding of 3D video

3D video employing depth-image-based rendering (DIBR) typically contains a stereo pair plus two associated depth maps. The stereo pair may be compressed asymmetrically (e.g., using mixed resolution coding) to effectively reduce the bit rate while maintaining the stereoscopic visual quality at the same level as that from symmetric coding. With depth information, it is straightforward to locate high-fidelity (HF) inter-view correspondences of pixels in the low-fidelity (LF) view. However, we find that LF-view fidelity cannot be consistently improved by simply substituting LF pixels with their HF counterparts, due to inter-view color/geometric differences and depth errors. In this paper, we propose an effective post-filter which first checks the coherence between local LF and HF waveforms to distinguish unreliable correspondences. Then, the LF view is adaptively rectified by the reliable HF pixels to improve its fidelity. Experimental results show that the proposed cross-view fidelity enhancement scheme can promote Peak Signal-to-Noise Ratio (PSNR) of LF views by up to 1.1 dB, and can effectively suppress ringing and blocking artifacts in flat regions of LF views.
Perceptually Lossless Image Coding Based on Foveated JND

Removing perceptual redundancy plays an important role in image compression. In this paper we developed a foveated just-noticeable-difference (FJND) model to quantify the perceptual redundancy in the image and integrate it in the H.265/HEVC intra encoding framework to provide a perceptually lossless image coding method. The experiments demonstrate that the proposed method effectively increases the compression performance.
Personalized Visual Saliency: Individuality Affects Image Perception

Due to the limited capability for information processing, humans only choose a small amount of input data received from visual field to better understand their environment. The selection of visual input implies the nonuniform distribution of visual attention, which is influenced by environmental visual stimuli and endogenous subject interest. Traditional saliency models do not differentiate individuals, exploring the common trend in attention deployment. This paper investigates individual nuance and association in both saccadic movements and attention distribution, and then discusses how individuality plays a role in predicting attention with low-level and deep features, respectively. It turns out that individual differences indeed exist and can be better discriminated by deep features. In conclusion, individuality not only contributes to improving the accuracy of attention prediction models but also gives us a hint about some interesting viewing behavior that stands out from the crowd pattern.
QoE-Driven Cache Management for HTTP Adaptive Bit Rate Streaming Over Wireless Networks

In this paper, we investigate the problem of optimal content cache management for HTTP adaptive bit rate (ABR) streaming over wireless networks. Specifically, in the media cloud, each content is transcoded into a set of media files with diverse playback rates, and appropriate files will be dynamically chosen in response to channel conditions and screen forms. Our design objective is to maximize the quality of experience (QoE) of an individual content for the end users, under a limited storage budget. Deriving a logarithmic QoE model from our experimental results, we formulate the individual content cache management for HTTP ABR streaming over wireless network as a constrained convex optimization problem. We adopt a two-step process to solve the snapshot problem. First, using the Lagrange multiplier method, we obtain the numerical solution of the set of playback rates for a fixed number of cache copies and characterize the optimal solution analytically. Our investigation reveals a fundamental phase change in the optimal solution as the number of cached files increases. Second, we develop three alternative search algorithms to find the optimal number of cached files, and compare their scalability under average and worst complexity metrics. Our numerical results suggest that, under optimal cache schemes, the maximum QoE measurement, i.e., mean-opinion-score (MOS), is a concave function of the allowable storage size. Our cache management can provide high expected QoE with low complexity, shedding light on the design of HTTP ABR streaming services over wireless networks.
Growth charts of root knots and giant cells caused by Meloidogyne incognita in susceptible tomato roots in vit

In this study, a repeatable and compatible interaction system between Meloidogyne incognita and tomato plants was firstly established by artificially inoculating sterilized second stage juvenile(J2) of M. incognita into root tips of 15-day tomato plant in vitro. Egg masses of female nematodes were obviously seen on root-knots after inoculation for 30 days at 27 °C in a growth chamber. Then, growth charts of root-knots and giant cells caused by M. incognita in tomato roots in vitro were studied as follows: Ten inoculated root tips were observed and collected each day for studying growth charts of root knots and giant cells by paraffin sections during 28 days after inoculation (dai). The biggest diameter of each-day root-knot and its giant cell size were measured. Totally, 280 effective root-knots and their giant cells in 1680 paraffin sections were studied. Growth charts drawn by the software of SAS (system for windows 9.0) indicated that the biggest diameter of root knot became bigger and bigger during the first 20 dai, and then keep almost the same size from 24 to 28 dai; meanwhile, its giant cell size became bigger and bigger in the first 14 dai, and then keep almost the same size from 14dai to 20 dai, at last became smaller and smaller after 20 dai. To be surprised, no giant cells were seen at 28 dai, while the female nematodes were seen with their egg masses in tomato root tissue. To our knowledge, it is a first report on 28- day growth charts of root knots and giant cells caused by M. incognita in tomato roots.
Image retargeting based on the sensitivity-tuned visual significance map

In this paper, we propose a novel image retargeting algorithm based on the sensitivity-tuned visual significance map which is composed of a saliency map and a gradient map. We develop a new saliency detection model based on the human visual sensitivity and amplitude spectrum of image patches. We use a coherent normalization based fusion method to combine the saliency map and the gradient map to generate the visual significance map. The seam carving technique is adopted for image retargeting, based on the sensitivity-tuned visual significance map. Experiment results show that the proposed algorithm outperforms the relevant state-of-the-arts image retargeting algorithms significantly.
Visual attention identification using random walks based eye tracking protocols

The identification of visual attention is an essential part of analyzing the user's visual perception behavior. In this paper, a novel measurement of the center in the fixation cluster based on random walks is developed. In the proposed method, we generate the cluster which includes fixations for a particular region-of-interest (ROI). We form a distance matrix by calculating the point-to-point distances in each cluster and discover the center of the fixation cluster utilizing random walks. Based on the obtained distance matrix, the matrix referring to their transition probability among each other is thereafter constituted. The density of the points in the cluster is also calculated. We utilize the density as the initial coefficient for each fixation and update the coefficients with the consideration of their transition probability and distribution in the cluster. Finally, the generated convergent coefficients are used to weight the fixations and produce the position of the center based on their weighted average. Experimental results demonstrate that the center of the fixation cluster produced by the proposed method outperforms the traditional mean position based method and describes the feature of the viewer's visual attention more accurately.
Spherical Domain Rate-Distortion Optimization for Omnidirectional Video Coding

Efficient compression of omnidirectional video is important for emerging virtual reality applications. To compress this kind of video, each frame is projected to a 2D plane [e.g., equirectangular projection (ERP) map] first, adapting to the input format of existing video coding systems. At the display side, an inverse projection is applied to the reconstructed video to restore signals in spherical domain. Such a projection, however, makes presentation and encoding in different domains. Thus, an encoder agnostic to the projection performs inefficiently. In this paper, we analyze how a projection influences the distortion measurements in different domains. Based on the analysis, we propose a scheme to optimize the encoding process based on signals’ distortion in spherical domain. With the proposed optimization, an average 4.31% (up to 9.67%) luma BD-rate reduction is achieved for ERP in random access configuration. The corresponding bit saving is averagely 10.84% (up to 34.44%) when considering the viewing field being $\pi /2$ . The proposed method also benefits other projections and viewport settings, with a marginal complexity increase.
Suppressing texture-depth misalignment for boundary noise removal in view synthesis

During view synthesis based on depth maps, also known as Depth-Image-Based Rendering (DIBR), annoying artifacts are often generated around foreground objects, yielding the visual effects that slim silhouettes of foreground objects are scattered into the background. The artifacts are referred as the boundary noises. We investigate the cause of boundary noises, and find out that they result from the misalignment between texture and depth information along object boundaries. Accordingly, we propose a novel solution to remove such boundary noises by applying restrictions during forward warping on the pixels within the texture-depth misalignment regions. Experiments show this algorithm can effectively eliminate most boundary noises and it is also robust for view synthesis with compressed depth and texture information.
Quality Assessment for Remote Sensing Images: Approaches and Applications

Remote sensing images play important roles in various applications, such as meteorology, geology, ecology, hydrology, and so on. The quality of remote sensing images is the key in the success of these applications. It provides the basis for classification, segmentation, compression, etc. In this paper, we aim to provide a review of quality assessment for remote sensing images. We introduce some background and preliminaries of remote sensing images and corresponding quality assessment. We classify the quality assessment methods into two categories, i.e., Imaging quality and processed image quality assessment methods. Specific methods are introduced individually. We also present a quality assessment application of Lands at 8 OLI images by discussing the impact of image quality on classification accuracy.
Depth No-Synthesis-Error Model for View Synthesis in 3-D Video

Currently, 3-D Video targets at the application of disparity-adjustable stereoscopic video, where view synthesis based on depth-image-based rendering (DIBR) is employed to generate virtual views. Distortions in depth information may introduce geometry changes or occlusion variations in the synthesized views. In practice, depth information is stored in 8-bit grayscale format, whereas the disparity range for a visually comfortable stereo pair is usually much less than 256 levels. Thus, several depth levels may correspond to the same integer (or sub-pixel) disparity value in the DIBR-based view synthesis such that some depth distortions may not result in geometry changes in the synthesized view. From this observation, we develop a depth no-synthesis-error (D-NOSE) model to examine the allowable depth distortions in rendering a virtual view without introducing any geometry changes. We further show that the depth distortions prescribed by the proposed D-NOSE profile also do not compromise the occlusion order in view synthesis. Therefore, a virtual view can be synthesized losslessly if depth distortions follow the D-NOSE specified thresholds. Our simulations validate the proposed D-NOSE model in lossless view synthesis and demonstrate the gain with the model in depth coding.
Maximum Wireless Power Transfer to the Implantable Device in the Radiative Near Field

This letter presents a wireless power transmission (WPT) system with enhanced power transmission efficiency (PTE) for powering implantable devices in the radiative near field. The gain of the implantable receiving antenna is improved by 8 dB through utilizing a parasitic patch. The system is further optimized by maximizing the PTE between the transmitting antenna array and the implantable receiving antenna, yielding an optimal excitation distribution for the transmitting antenna array. Compared to a reference WPT system with the transmitting array fed by the uniform amplitude and phase, the PTE of the optimized system is about 1.3 times higher than that of the reference one.
3-D BLE Indoor Localization Based on Denoising Autoencoder

Bluetooth low energy (BLE)-based indoor localization has attracted increasing interests for its low-cost, low-power consumption, and ubiquitous availability in mobile devices. In this paper, a novel denoising autoencoder-based BLE indoor localization (DABIL) method is proposed to provide high-performance 3-D positioning in large indoor places. A deep learning model, called denoising autoencoder, is adopted to extract robust fingerprint patterns from received signal strength indicator measurements, and a fingerprint database is constructed with reference locations in 3-D space, rather than traditional 2-D plane. Field experiments show that 3-D space fingerprinting can effectively increase positioning accuracy, and DABIL performs the best in terms of both horizontal accuracy and vertical accuracy, comparing with a traditional fingerprinting method and a deep learning-based method. Moreover, it can achieve stable performance with incomplete beacon measurements due to unpredictable BLE beacon lost.
Remote Sensing Image Compression: A Review

With the increasing spatial and temporal resolutions of acquired remote sensing (RS) images, effective image compression is becoming more and more important. RS image compression technologies have been extensively studied in the past a few decades, and various algorithms have been developed accordingly. In this paper, we provide an overview of practically deployed RS image compression approaches, including predictive coding and transform coding approaches that have been adopted in different satellite systems. In addition, some newly derived RS image compression methods are discussed, with highlights on the new trends of the on-going design and developments of RS image compression.
Recent advances in perceptual H.265/HEVC video coding

H.265/High Efficiency Video Coding (HEVC), the latest video coding standard developed jointly by ITU-T and by ISO/IEC in the joint collaborative team on video coding (JCT-VC), has shown significant objective compression improvements over the last video coding standard H.264/AVC. After the establishment of H.265/HEVC, more and more attention has been paid on how to further improve the visual quality such that better coding efficiency could be achieved. In this paper, we review the progress of subjective optimization of H.265/HEVC based video coding systems and summarize the recent advances of perceptual H.265/HEVC video coding. Referring to our earlier work, we classify the current perceptual H.265/HEVC video coding technologies into two categories, vision-model based approach and signal-driven approach. Moreover, due to the new technologies such as Coding Tree Unit (CTU) structure and Sample Adaptive Offset (SAO). The future research directions for perceptual H.265/HEVC video coding are also discussed.
CNN-based rate-distortion modeling for H.265/HEVC

In this paper, we propose a convolutional neural network (CNN)-based rate-distortion (R-D) modeling method for H.265/HEVC. A fully convolutional neural network (CNN) is designed to learn end-to-end, pixels-to-pixels mappings from the original images to the structural similarity (SSIM) maps indicating distortion. The rate information is predicted through a CNN with fully connected layers as well. When compared to traditional CNN methods, the proposed mappings to the distortion or rate information. The experiments demonstrate the feasibility of our CNN-based framework for rate-distortion modeling.
Individual trait oriented scanpath prediction for visual attention analysis

Scanpath refelects the shift of visual attention, therefore prediction of scanpath plays an important role in image analysis and understanding. However traditional scanpath prediction methods ignore the individuality of subjects such as oculomotor bias and other relevant factors. Hence, to make the scanpath prediction more accurate, we incorporate individual traits into a universal scanpath prediction framework for the subject based on the saccade distribution and factor weighting. Experiments demonstrate that our model improves the predicting performance, which proves that individuality is an important factor in scanpath prediction.
Fingerprint image enhancement using weak models

Biometrics-based authentication and identification systems have to handle images acquired in noisy and hostile environments. The signal quality is assessed to decide if there is sufficient signal strength to process further. Poor quality signals require "enhancement" before further processing of the input signal. Often enhancement implies creating a more visibly pleasing image. However, biometrics signals need to improve the image quality for machine processability. This means that the enhancement algorithm should have some weak model about the sample (image) formation process. Enhancement is then some type of "normalization" or "beautification". We present a weak model-based image enhancement algorithm for fingerprint images. The results of the proposed algorithm are presented in terms of the improvements in the overall system performance measured in terms of a receiver operating characteristics curve.
Robust fingerprint authentication using local structural similarity

Fingerprint matching is challenging as the matcher has to minimize two competing error rates: the False Accept Rate and the False Reject Rate. We propose a novel, efficient, accurate and distortion-tolerant fingerprint authentication technique based on graph representation. Using the fingerprint minutiae features, a labeled, and weighted graph of minutiae is constructed for both the query fingerprint and the reference fingerprint. In the first phase, we obtain a minimum set of matched node pairs by matching their neighborhood structures. In the second phase, we include more pairs in the match by comparing distances with respect to matched pairs obtained in first phase. An optional third phase, extending the neighborhood around each feature, is entered if we cannot arrive at a decision based on the analysis in first two phases. The proposed algorithm has been tested with excellent results on a large private livescan database obtained with optical scanners.
Cardiac anatomy as a biometric

In this study, we propose a novel biometric signature for human identification based on anatomically unique structures of the left ventricle of the heart. An algorithm is developed that analyzes the 3 primary anatomical structures of the left ventricle: the endocardium, myocardium, and papillary muscles. Comparisons of these analyses between probe and gallery images produces a similarity score that is used as the basis of the biometric. The performance of the algorithm is tested on a cohort of 10 de-identified subjects imaged by Cardiac MRI. Perfect matching between individuals is obtained with good separation between the genuine and impostor classes. In summary, this study demonstrates using anatomy of the left ventricle of the human heart for the purposes of a biometric signature.
Anonymous and Revocable Fingerprint Recognition

Biometric identification has numerous advantages over conventional ID and password systems; however, the lack of anonymity and revocability of biometric templates is of concern. Several methods have been proposed to address these problems. Many of the approaches require a precise registration before matching in the anonymous domain. We introduce binary string representations of fingerprints that obviates the need for registration and can be directly matched. We describe several techniques for creating anonymous and revocable representations using these binary string representations. The match performance of these representations is evaluated using a large database of fingerprint images. We prove that given an anonymous representation, it is computationally infeasible to invert it to the original fingerprint, thereby preserving privacy. To the best of our knowledge, this is the first linear, anonymous and revocable fingerprint representation that is implicitly registered.
Improving classifier fusion via Pool Adjacent Violators normalization

Classifier fusion is a well-studied problem in which decisions from multiple classifiers are combined at the score, rank, or decision level to obtain better results than a single classifier. Subsequently, various techniques for combining classifiers at each of these levels have been proposed in the literature. Many popular methods entail scaling and normalizing the scores obtained by each classifier to a common numerical range before combining the normalized scores using the sum rule or another classifier. In this research, we explore an alternative method to combine classifiers at the score level. The Pool Adjacent Violators (PAV) algorithm has traditionally been utilized to convert classifier match scores to confidence values that model posterior probabilities for test data. The PAV algorithm and other score normalization techniques have studied the same problem without being aware of each other. In this first ever study to combine the two, we propose the PAV algorithm for classifier fusion on publicly available NIST multi-modal biometrics score dataset. We observe that it provides several advantages over existing techniques and find that the interpretation learned by the PAV algorithm is more robust than the scaling learned by other popular normalization algorithms such as min-max. Moreover, the PAV algorithm enables the combined score to be interpreted as confidence and is able to further improve the results obtained by other approaches. We also observe that utilizing traditional normalization techniques first for individual classifiers and then normalizing the fused score using PAV offers a performance boost compared to only using the PAV algorithm.
Multi-biometric cohort analysis for biometric fusion

Biometric matching decisions have traditionally been made based solely on a score that represents the similarity of the query biometric to the enrolled biometric(s) of the claimed identity. Fusion schemes have been proposed to benefit from the availability of multiple biometric samples (e.g., multiple samples of the same fingerprint) or multiple different biometrics (e.g., face and fingerprint). These commonly adopted fusion approaches rarely make use of the large number of non-matching biometric samples available in the database in the form of other enrolled identities or training data. In this paper, we study the impact of combining this information with the existing fusion methodologies in a cohort analysis framework. Experimental results are provided to show the usefulness of such a cohort-based fusion of face and fingerprint biometrics.