nils-herrmann/clean_scirepeval huggingface dataset:
Blind Quality Assessment Based on Pseudo-Reference Image

Traditional full-reference image quality assessment (IQA) metrics generally predict the quality of the distorted image by measuring its deviation from a perfect quality image called reference image. When the reference image is not fully available, the reduced-reference and no-reference IQA metrics may still be able to derive some characteristics of the perfect quality images, and then measure the distorted image's deviation from these characteristics. In this paper, contrary to the conventional IQA metrics, we utilize a new “reference” called pseudo-reference image (PRI) and a PRI-based blind IQA (BIQA) framework. Different from a traditional reference image, which is assumed to have a perfect quality, PRI is generated from the distorted image and is assumed to suffer from the severest distortion for a given application. Based on the PRI-based BIQA framework, we develop distortion-specific metrics to estimate blockiness, sharpness, and noisiness. The PRI-based metrics calculate the similarity between the distorted image's and the PRI's structures. An image suffering from severer distortion has a higher degree of similarity with the corresponding PRI. Through a two-stage quality regression after a distortion identification framework, we then integrate the PRI-based distortion-specific metrics into a general-purpose BIQA method named blind PRI-based (BPRI) metric. The BPRI metric is opinion-unaware (OU) and almost training-free except for the distortion identification process. Comparative studies on five large IQA databases show that the proposed BPRI model is comparable to the state-of-the-art opinion-aware- and OU-BIQA models. Furthermore, BPRI not only performs well on natural scene images, but also is applicable to screen content images. The MATLAB source code of BPRI and other PRI-based distortion-specific metrics will be publicly available.
Generalized Equalization Model for Image Enhancement

In this paper, we propose a generalized equalization model for image enhancement. Based on our analysis on the relationships between image histogram and contrast enhancement/white balancing, we first establish a generalized equalization model integrating contrast enhancement and white balancing into a unified framework of convex programming of image histogram. We show that many image enhancement tasks can be accomplished by the proposed model using different configurations of parameters. With two defining properties of histogram transform, namely contrast gain and nonlinearity, the model parameters for different enhancement applications can be optimized. We then derive an optimal image enhancement algorithm that theoretically achieves the best joint contrast enhancement and white balancing result with trading-off between contrast enhancement and tonal distortion. Subjective and objective experimental results show favorable performances of the proposed algorithm in applications of image enhancement, white balancing and tone correction. Computational complexity of the proposed method is also analyzed.
Information security display system based on Spatial Psychovisual Modulation

Privacy protection is of increasing importance in this era of information explosion. This paper introduces an information security display system based on the idea of Spatial Psycho-visual Modulation (SPVM). With the rapid advance of modern manufacturing techniques, display devices now support very high pixel density (e.g. the retina display of Apple). Meanwhile the human visual system (HVS) cannot distinguish image signals with spatial frequency above a threshold, as predicted by the contrast sensitivity function (CSF). Therefore, it is now possible for us to devise a type of information security display using the mismatch between resolutions of modern display devices and the HVS. Given the desired visual stimuli for both the bystanders and the authorized users, we propose a method to design display signals accordingly. We select polarization as a way to effectively differentiate the bystanders and the authorized users. Operationally, applying complementary polarization to light emitted from different spatial sections of the display could in itself be a challenge. Fortunately, the development of stereoscopic display technologies has made available a lot of polarization based spatial multiplexing type of display devices. Hardware of the information security display system is based on a polarization based stereoscopic screen made by LG. Software of the information security display system is written in C++ with SDKs of DirectX and etc. Kinect is also included into our system to enhance the experience of human-computer interaction. Extended experimental results will be given in this paper to justify the effectiveness and robustness of the system. The developed system serves both as a proof-of-concept of the SPVM method, as well as a test bed for future research of SPVM based display technology.
Lossless predictive coding with Bayesian treatment

Natural image statistics have been widely exploited for lossless predictive coding and other applications. However, traditional adaptive techniques always focus on the local consistency of training set regardless of what the predicted target looks like. We investigate the problem of introducing the model evidence of predicted target since self-similarity inherent in natural images gives some kind of prior information for the distribution of predicted result. The proposed Bayesian model integrated with both training evidence and target evidence takes full advantages of local structure as well as self-similarity. Experimental results demonstrate that the proposed context model achieves best results compared with the state-of-the-art lossless predictors.
Quality assessment for out-of-focus blurred images

During the process of image acquisition, images are often subject to out-of-focus or defocus blur because of the improper adjustment of the camera's focal length, this image blur will degrade the image quality. However, in the literature, image quality assessment (IQA) methods dedicated to evaluating the quality of images with out-of-focus blur remain few. Therefore, in this paper, we focus our attention on the quality assessment of images that suffer from out-of-focus blur and propose an objective quality assessment method accordingly. Concretely, we construct a dedicated out-of-focus blurred image dataset, which is composed of 150 images subjected to different degrees of out-of-focus blur and the mean opinion scores (MOSs). Then, we propose a specific objective quality metric for the blurred images, which combines image sharpness assessment and saliency-guided pooling strategy. Experimental results demonstrate the proposed metric highly correlates with human judgements of image quality.
Fast and robust questionnaire recognition on mobile device

Questionnaires are widely used for investigation and statistical analysis. However, paper-based questionnaires require great human resources to count the statistical results or enter data into database, which are time consuming. A system capable of recognizing results of questionnaires will be very useful in many aspects. In this paper, we develop a fast and robust digital recognition system for questionnaire on mobile device. The system trains a questionnaire classifier and detects the questionnaire at first. Then it calibrates the detected questionnaire through matrix transform, and digitizes the options of the questionnaire with the improved binarization method. Finally, it determines the chosen options by duty ration of each option and outputs the content of the selected options for statistical analysis and data entry. The system is written in C++ and Java language with libraries of OpenGL and OpenCV on Android platform. In our experiments, the system has a high speed for identification and a high accuracy for recognition in the complicated background.
A Flash Light System for Individuals with Visual Impairment Based on TPVM

We propose a flashlight system to aid visually impaired people using the paradigm of temporal psychovisual modulation (TPVM), which is new display mode taking advantage of the limited flicker fusion frequency of human eyes and high refresh rate of modern display devices to achieve visual bifurcation effect. Structured light in visible spectrum is projected out onto the road surface and a synchronized camera detects the deformation and then the recognition system calculates the road flatness (e.g. smooth road, up-or down-stairs). To minimize the visual disturbance to other people, the TPVM display technique effectively conceals the structured light and making it basically a normal flashlight to normal observers. The system works in visible spectrum to minimize cost of the camera and the projector. We design fast and reliable recognition system with iterative dichotomiser 3(ID3) algorithm to differentiate condition of the pavement ahead into smooth road, wall ahead and up/down-stairs. Experimental results are presented to validate the proposed system.
Image coding with overlapped projection and pyramid vector quantization

A new effective still image coding scheme based on overlapped projections and pyramid vector quantization is presented. In standard image coding the spatial redundancy of the images is reduced subdividing the incoming data into small blocks and applying to these a linear transformation (DCT) to obtain highly independent coefficients, that are scalar quantized. This block by block coding takes advantage of the spatial correlation but it induces visually annoying blocking artifacts (especially at high compression ratios). The authors resent a different coding scheme where the image is subdivided into circular partially overlapped regions and then the projections along predefined directions are determined and vector quantized. Simulations show that the projection technique allows the detection of the most important information locally present on the image while the use of circular overlapped regions reduces blocking artifacts.<<ETX>>
HRTF personalization based on weighted sparse representation of anthropometric features

Personalized head-related transfer functions (HRTFs) are essential for presenting authentic spatial audio through binaural rendering. However, measuring personalized HRTFs for every user is a tedious task and requires specialized equipment. This paper presents an easy and efficient method for obtaining personalized magnitude response of HRTFs. It treats the problem of HRTF synthesis as finding the sparse representation of the anthropometric features of the new listener with respect to anthropometric features of the user set in the CIPIC database. Unlike the previous sparse representation methods, our method assigns different weights to different anthropometric features depending on their relevance. We compared our approach with state of the art sparse representation and closest-match based approaches. The results show that our approach outperforms the previous approaches resulting an average spectral distortion value of 5.53 dBs between synthesized and actual HRTFs for all users present in the CIPIC database.
A phylogenetic analysis of near-duplicate audio tracks

We present a content-based system for the analysis of near-duplicate audio tracks. The objective is to infer the structure of modifications, represented as trees, underneath a pool of near-duplicates, possibly specifying the operations the tracks have gone through. A pilot study was carried out for a set of plausible processing operators, including trim, fade and perceptual audio coding, generating near-duplicates from an original audio track. The proposed method measures the similarity between pairs of near-duplicates and reconstructs a tree representing the causal dependencies in the analyzed pool. Experimental results demonstrate that the structure of the tree can be successfully recovered, also in the challenging case in which some pieces of information are missing, i.e., when only a subset of near-duplicate tracks is available.
A Geometrical–Statistical Approach to Outlier Removal for TDOA Measurements

The curse of outlier measurements in estimation problems is a well-known issue in a variety of fields. Therefore, outlier removal procedures, which enables the identification of spurious measurements within a set, have been developed for many different scenarios and applications. In this paper, we propose a statistically motivated outlier removal algorithm for time differences of arrival (TDOAs), or equivalently range differences (RD), acquired at sensor arrays. The method exploits the TDOA-space formalism and works by only knowing relative sensor positions. As the proposed method is completely independent from the application for which measurements are used, it can be reliably used to identify outliers within a set of TDOA/RD measurements in different fields (e.g., acoustic source localization, sensor synchronization, radar, remote sensing, etc.). The proposed outlier removal algorithm is validated by means of synthetic simulations and real experiments.
Adaptive vector quantization for picture coding using neural networks

The paper presents applications of neural network algorithms to the design of an adaptive vector quantizer. Vector quantization has been applied to the problem of displaying natural images with a reduced set of colors (colormap) and to the interframe coding of image sequences. The first step was to test classical Linde Buzo Gray (LGB), self organizing feature maps (SOFM) and Competitive Learning (CL) algorithms for the codebook design. The best results for the reconstructed quality image and the computational time are obtained using a CL algorithm with a new initialization strategy that solves the problem of underutilized nodes. An adaptive vector quantization algorithm is proposed and tested in a motion compensated image coder. The results of the simulations are very promising. In fact the coder performance, compared with that using a fixed VQ, is considerably improved and the subjective quality of the coded images is much better than that obtained using standard vector quantization, especially when rapid motion is present in the scene.
Phylogeny reconstruction for misaligned and compressed video sequences

In the last few years, the amount of videos distributed online has dramatically increased due to the popularity of media sharing platforms (e.g., YouTube, Vimeo, etc.). However, distributed videos are often edited copies of original content, typically referred to as near duplicates. In this paper, we face the problem of reconstructing a video phylogeny tree, i.e., given a set of near-duplicate videos, we want to reconstruct the relationships between every pair of videos to detect which one generated the others and trace back their evolution history. Solving this problem is of paramount importance when the first published video within a set is sought, e.g., to solve copyright infringement cases or to pinpoint criminal impersonation online. The technique we propose exploits the same rationale of previous works in the field of image and video phylogeny. However, we embed in the commonly used pipeline of operations the possibility of dealing with temporally misaligned and encoded video sequences, thus making the method applicable to user-generated videos shared on online platforms. Results computed on a wide dataset of video sequences highlight the importance of taking care of both coding and misalignment in the reconstruction pipeline.
Symmetric Distributed Coding of Stereo Video Sequences

In this paper we present a novel video coding scheme to compress stereo video sequences. We consider a wireless sensor network scenario, where the sensing nodes cannot communicate with each other and are characterized by limited computational complexity. The joint decoder exploits both the temporal and inter-view correlation to generate the side information. To this end, we propose a fusion algorithm that adaptively selects either the temporal or the inter-view side information on a pixel-by-pixel basis. In addition, the coding algorithm is symmetric with respect to the two cameras. We also propose a practical stopping criterion for turbo decoding that determines when decoding is successful. Experimental results on stereo video sequences show that a coding efficiency gain up to 4dB can be obtained by the proposed scheme at high bit-rates.
Soundfield Imaging in the Ray Space

In this work we propose a general approach to acoustic scene analysis based on a novel data structure (ray-space image) that encodes the directional plenacoustic function over a line segment (Observation Window, OW). We define and describe a system for acquiring a ray-space image using a microphone array and refer to it as ray-space (or “soundfield”) camera. The method consists of acquiring the pseudo-spectra corresponding to a grid of sampling points over the OW, and remapping them onto the ray space, which parameterizes acoustic paths crossing the OW. The resulting ray-space image displays the information gathered by the sensors in such a way that the elements of the acoustic scene (sources and reflectors) will be easy to discern, recognize and extract. The key advantage of this method is that ray-space images, irrespective of the application, are generated by a common (and highly parallelizable) processing layer, and can be processed using methods coming from the extensive literature of pattern analysis. After defining the ideal ray-space image in terms of the directional plenacoustic function, we show how to acquire it using a microphone array. We also discuss resolution and aliasing issues and show two simple examples of applications of ray-space imaging.
Three-view camera calibration using geometric algebra

In a former work of ours C. Defferara et al. (2002), we proposed a new way to express and interpret the epipolar constraint using Geometric Algebra, and we derived from it a novel and efficient 2-view camera calibration technique. In this paper we extend this GA approach to the 3-view case. After expressing the trifocal constraint in terms of bivectors and trivectors, we provide an alternative geometric interpretation of the coefficients of the trifocal tensor. On the basis of that, we propose a novel solution for the simultaneous determination of the focal lengths of the cameras and the rigid motion between three views.
Self-calibration of microphone arrays from measurement of Times of Arrival of acoustic signals

In this paper we present a methodology for the self-calibration of microphone arrays using Times of Arrival of acoustic signals produced by acoustic sources. Geometric constraints are derived from the measurements of the Times of Arrival. Microphone locations are then found by combining the geometric constraints. When multiple arrays are present in the acoustic scene, in many applications the knowledge of their mutual positions is required. If a priori information about the geometry of the array is given, the same framework can be easily adapted to infer the mutual positions of the arrays. Some experimental results and simulations prove the accuracy of the current work.
Near-duplicate detection and alignment for multi-view videos

The increasing popularity of video sharing platforms (e.g., YouTube, Vimeo, etc.) has determined the widespread diffusion of near-duplicate videos, i.e., sequences obtained applying different editing operations to the same original clip. However, it is also possible to come across sequences referring to the same specific event shot from different viewpoints. This is a very common situation that arises when analyzing user-generated content acquired with mobile devices. Therefore, for some applications, it can be useful to extend the concept of near-duplicates considering also all the videos (and their edited versions) referring to the same event even if shot from different viewpoints. In this paper we consider such challenging scenario. More specifically, we focus on the problem of multi-view near-duplicate video detection and temporal alignment. In doing so, we show the limitations of a state-of-the-art algorithm based on robust hashing, and propose a processing pipeline that allows to deal also with sequences taken from significantly different viewpoints.
Watermarking for musical pieces indexing used in automatic cue sheet generation systems

We propose a fast and robust watermarking scheme for digital audio signals that allows automatic identification of musical pieces transmitted in TV broadcast programs. Usually the watermark is used as a way for hiding information on digital media. The watermarked information may allow copyright protection or user identification and media indexing. In our application the watermark must be, obviously, imperceptible to the users to maintain the quality of the source, should be robust to standard TV and radio editing and its processing has to require a very low complexity. This last item is essential to allow a software real-time implementation of the insertion and detection of watermark using only a minimum amount of the computation power of a modern PC. In the proposed algorithm we divide the audio signal in frames, on each frame a masking function is computed based on the human auditory system (HAS), and the watermark is embedded using a spread spectrum technique. Simulation results obtained on a long set of subjective tests prove the quality and the robustness of the proposed approach.
Invariant action classification with volumetric data

We propose an action recognition algorithm in which the image sequences capturing a moving human body produced by a significant number of cameras are first used to generate a volumetric representation of the body by means of volumetric intersection. Classification is then performed directly on 3D data, making the system inherently insensitive to viewpoint dependence and motion trajectory variability. Suitable features are extracted from the voxset approximating the body, and fed to a hidden Markov model to produce a finite-state description of the motion. The Kullback-Leibler distance is finally used to classify new sequences.
Extraction of Acoustic Sources Through the Processing of Sound Field Maps in the Ray Space

Our goal is to develop a model-based approach to acoustic source extraction from microphone array data, which is suitable for both near-field and far-field sources. A signal representation based on plane-wave (PW) decomposition is suitable for acoustic sources in the far field as the resulting spectrum turns out to be impulsive. When the source approaches the array, however, the curvature of the wavefront causes the spectrum of the PW components to depart from impulsive behavior, thus making source extraction harder to attain. In this paper, we adopt a sound field representation based on the local estimation of the plenacoustic function along the array line. This approach consists of dividing the array into subarrays, and applying the PW analysis on individual subarrays. This has the immediate result of extending the range of validity of the far-field hypothesis, as a source that enters the near-field range of the extended array is still in the far-field range of the subarrays. PW analysis on subarrays allows us to construct the so-called sound field map in a domain of acoustic visibility called ray space. The extraction of the desired source is accomplished through spatial filtering of the sound field map. The design of the spatial filter relies on a linear minimum mean square error criterion defined on the sound field map. The effectiveness of the proposed methodology is proven through an extensive simulation campaign as well as real experiments.