nils-herrmann/clean_scirepeval huggingface dataset:
Scrambling-based tool for secure protection of JPEG images

JPEG scrambling tool is a flexible web-based tool with an intuitive and simple to use GUI and interface to secure visual information in a region of interest (ROI) of JPEG images. The tool demonstrates an efficient integration and use of security tools in JPEG image format by an example of scrambling privacy filter, which enables a variety of security services such as confidentiality, integrity verification, source authentication, and conditional access.
Visual data compression for multimedia applications

The compression of visual information in the framework of multimedia applications is discussed. To this end, major approaches to compress still as well as moving pictures are reviewed. The most important objective in any compression algorithm is that of compression efficiency. High-compression coding of still pictures can be split into three categories: waveform, second-generation, and fractal coding techniques. Each coding approach introduces a different artifact at the target bit rates. The primary objective of most ongoing research in this field is to mask these artifacts as much as possible to the human visual system. Video-compression techniques have to deal with data enriched by one more component, namely, the temporal coordinate. Either compression techniques developed for still images can be generalized for three-dimensional signals (space and time) or a hybrid approach can be defined based on motion compensation. The video compression techniques can then be classified into the following four classes: waveform, object-based, model-based, and fractal coding techniques. This paper provides the reader with a tutorial on major visual data-compression techniques and a list of references for further information as the details of each method.
Towards subjective quality assessment of point cloud imaging in augmented reality

Recently, there has been an increased interest in capture, processing and rendering of visual content in form of point clouds. Among other challenges, subjective and objective quality assessments of point clouds are still open problems. Most proposed subjective quality evaluation methodologies are variants or extensions of counter parts from conventional approaches such as those proposed in various ITU-R and ITU-T recommendations. A key issue with point cloud content is that of rendering and display devices which are thoroughly different from those in other modalities in addition to novel applications which depart from traditional display devices. In this paper, we propose a radically different approach to point cloud subjective quality assessment for point cloud by making use of augmented reality head mounted displays. Beside description of the approach, we show examples of implementation of the proposed methodology and draw conclusions regarding its advantages and drawbacks. Finally, the proposed approach is used in assessing the performance of widely used objective metrics to compute quality of point cloud contents when they undergo various types of distortions such as corruption by noise, simplification and compression.
A VLSI filter architecture for digital HDTV codecs

The authors describe a compact VLSI implementation of fast pipelined two-dimensional FIR filters. A polyphase architecture is presented. The filter bank makes special use of coefficients which are in powers-of-two. This leads to an interest scheme where only shift and add operations are compared. A novel design strategy has been used to speed up the algorithm, as well as a novel adder. Internally, the speed achieved is around 200 MHz.<<ETX>>
The impact of expertise on brain computer interface based salient image retrieval

Autonomous decision making modules in computer vision application allow recognition and classification of different objects, persons, and events in images and video sequences and also make it possible to classify different sensor readings (e.g. images) according to their scientific saliencies. In this paper, we propose a new approach to create the training set for these algorithms by retrieving salient images using electroencephalogram (EEG) and brain computer interface (BCI) and rapid image presentation. To this end, two groups of subjects, namely, expert and novice subjects were asked to participate in our experiments. We show that a relatively high retrieval accuracy can be achieved for most of the subjects. Furthermore, to assess the impact of expertise on the retrieval process, we study their EEG signals separately and show that there is a clear difference in their brainwaves while observing salient images.
On Evaluating Video Object Segmentation Quality: A Perceptually Driven Objective Metric

The task of extracting objects in video sequences emerges in many applications such as object-based video coding (e.g., MPEG-4) and content-based video indexing and retrieval (e.g., MPEG-7). The MPEG-4 standard provides specifications for the coding of video objects, but does not address the problem of how to extract foreground objects in image sequences. Therefore, for specific applications, evaluating the quality of foreground/background segmentation results is necessary to allow for an appropriate selection of segmentation algorithms and for tuning their parameters for optimal performance. Many segmentation algorithms have been proposed along with a number of evaluation criteria. Nevertheless, formal psychophysical experiments evaluating the quality of different video foreground object segmentation results have not yet been conducted. In this paper, a generic framework for both subjective and objective segmentation quality evaluation is presented. An objective quality assessment method for segmentation evaluation is derived on the basis of perceptual factors through subjective experiments. The performance of the proposed method is shown on different state-of-the-art foreground/background segmentation algorithms and our method is compared to other objective methods which do not include perceptual factors. Moreover, on the basis of subjective results, weighting strategies are introduced into the proposed metric to meet the specificity of different segmentation applications e.g., video compression, video surveillance and mixed reality. Experimental results confirm the efficiency of the proposed approach.
Privacy protection of tone-mapped HDR images using false colours

High dynamic range (HDR) imaging has been developed for improved visual representation by capturing a wide range of luminance values. Owing to its properties, HDR content might lead to a larger privacy intrusion, requiring new methods for privacy protection. Previously, false colours were proved to be effective for assuring privacy protection for low dynamic range (LDR) images. In this work, the reliability of false colours when used for privacy protection of HDR images represented by tone-mapping operators (TMOs) is studied. Two different TMO techniques are tested, a simple TMO based on the Gamma transform and a more complex local TMO. Moreover, two false colour palettes are also tested, and are applied to images that result from both TMOs and also to an LDR image that represents the centre exposure in the image sequence used to create the HDR image. The degree of privacy protection is analysed through both a subjective test using crowdsourcing and an objective test using face recognition algorithms. It is concluded that the application of the two studied false colour palettes reduces the recognition accuracy with respect to both tests.
Electroencephalogram alterations during perception of pleasant and unpleasant odors

The olfactory system enables humans and many animals recognize and categorize different odors and can determine many behavioral and social reactions. For human beings, odor stimuli are highly associated with many processes such as emotions, attraction, mood, etc. One approach to understanding the olfaction is to monitor and analyze human brain activity during perception of odors. In this paper, we analyze electroencephalogram (EEG) of five participants during perception of unpleasant and pleasant odor stimuli. We identify the regions of the brain cortex that are active during discrimination of unpleasant and pleasant odor stimuli. We also show that, classification of EEG signals during perception of odors can reveal the pleasantness of the odor with relatively high accuracy.
Iterative interference cancellation for a coded multicarrier frequency-hopping CDMA (MC-FH-CDMA) system

In this paper, we consider a low complexity iterative multiuser receiver for the coded MC-FH-CDMA system, introduced in M. Ebrahimi T.M. and M. Nasiri-Kenari (May 2002). The structure of the proposed receiver consists of a multiuser likelihood calculator (MULC) and a bank of SISO channel decoders one for each active user. Based on the received signal and a priori information about code bits provided by SISO decoders in the previous iteration, MULC computes extrinsic information of coded symbols of active users in the form of log-likelihood ratio (LLR). This information is used by channel decoders as a priori information to compute an update of LLRs. The processing proceeds in an iterative manner similar to the decoding of turbo codes. We evaluate the performance of the proposed receiver in AWGN channels. Our numerical results show that the new receiver significantly improves the system performance. After a few iterations, the receiver reaches its ultimate performance, which is very close to the single-user performance.
DEAP: A Database for Emotion Analysis ;Using Physiological Signals

We present a multimodal data set for the analysis of human affective states. The electroencephalogram (EEG) and peripheral physiological signals of 32 participants were recorded as each watched 40 one-minute long excerpts of music videos. Participants rated each video in terms of the levels of arousal, valence, like/dislike, dominance, and familiarity. For 22 of the 32 participants, frontal face video was also recorded. A novel method for stimuli selection is proposed using retrieval by affective tags from the last.fm website, video highlight detection, and an online assessment tool. An extensive analysis of the participants' ratings during the experiment is presented. Correlates between the EEG signal frequencies and the participants' ratings are investigated. Methods and results are presented for single-trial classification of arousal, valence, and like/dislike ratings using the modalities of EEG, peripheral physiological signals, and multimedia content analysis. Finally, decision fusion of the classification results from different modalities is performed. The data set is made publicly available and we encourage other researchers to use it for testing their own affective state estimation methods.
Implicit retrieval of salient images using Brain Computer Interface

Space missions are often equipped with several high definition sensors that can autonomously collect a potentially enormous amount of data. The bottleneck in retrieving these often precious datasets is the onboard data storing capability and the communication bandwidth, which limit the amount of data that can be sent back to Earth. In this paper, we propose a method based on the analysis of brain electrical activity to identify the scientific interest of experts towards a given image in a large set of images. Such a method can be used to efficiently create an abundant training set (images and whether they are scientifically interesting) with a considerably faster image presentation rate that can go beyond expert consciousness, with less interrogation time for experts and relatively high performance.
Multivariate spectral analysis for identifying the brain activations during olfactory perception

Olfactory perception is a complex phenomenon associated with other processes such as cognition and emotion. Due to this complexity, there are still open issues and challenges regarding olfactory psychophysiology. One challenge concerns the investigation of the hedonic dimension of olfaction, and how it affects the power of the brain oscillations. Although there are some EEG studies exploring the changes in the power of the brain oscillations during olfactory perception, they use simple power spectral analysis techniques and vary much in terms of the reported findings. To reduce this variability, we propose the use of multivariate spectral analysis, to reveal only the frequency patterns of the EEG signals that contribute the most to olfactory perception. The goal is to investigate how these frequency patterns are affected by hedonically different odors throughout the cortex.
Objective quality metrics for video scalability

Scalable video coding is emerging as an efficient alternative to simulcast encoding to distribute the same video content simultaneously to many users having different terminals and network conditions. In order to select the best combination of video scalability options for a given network condition and content, the availability of objective metrics that can reliably predict the video quality of scalable video sequence is crucial. In this paper, we propose a performance evaluation study of a set of state of the art Full-Reference and No-Reference metrics, considering a public database of test video sequences and related subjective quality annotations. The results demonstrate that most of the considered metrics show lack of robustness as predictors of subjective quality when both spatial and temporal quality distortions occur.
Matching error based criterion of region merging for joint motion estimation and segmentation techniques

This paper describes a region merging method for joint motion estimation and segmentation of digital video sequences. The region merging criterion is based on the measure of the matching error for a region when applying a previously estimated motion to it. A region adjacency graph is used for data representation, which allows a scan independent processing and gives a high-level view. The method is simple-shape object-oriented and starts from a block-based segmentation. The aim of the proposed technique is to define simple shaped objects in a scene using motion information and a simple test.
Influence of audio-visual attention on perceived quality of standard definition multimedia content

When human subjects assess the quality of multimedia data, high level perceptual processes such as Focus of Attention (FoA) and eye movements are believed to play an important role in such tasks. While prior art reports incorporation of visual FoA into objective quality metrics, audio-visual FoA has been rarely addressed and utilized in spite of the importance and presence of both audio and video information in many multimedia systems. This paper explores the influence of audio-visual FoA in the perceived quality of standard definition audio-visual sequences. Results of a subjective quality assessment study are reported, where it is shown that the sound source attracts visual attention and thereby the visual degradation in the regions far from the source is less perceived when compared to sound-emitting regions.
Photo-realistic 3D model coding in MPEG-4

The working group ISO/IEC JTC1/SC29/WG11, better known as MPEG (Motion Picture Experts Group), recently adopted a new standard called MPEG-4, which provides solutions for coding of audio-visual objects of a natural and synthetic nature. This paper describes the tools and technologies used in the MPEG-4 standard which allow for efficient compression of photo-realistic three dimensional models. Besides compression efficiency, issues such as streaming, robustness to errors, low complexity and memory usage were taken into account when designing such technologies.
Subjective quality assessment of scalable video coding: A survey

Scalable video coding is a promising solution for efficient video content distribution to users having heterogeneous network and terminal capabilities. Thanks to its inherent multidimensional adaptability, a scalable bit stream can be used to simultaneously transmit multiple video sequences having different bit rates to corresponding target users, without necessity of re-encoding or transcoding. In order to exploit such an advantage effectively, it is crucial to understand the effects of multidimensional scalability options on the perceived quality and the trade-off between the scalability dimensions through subjective quality assessment. This paper reviews existing studies aiming at achieving this goal in order to summarize their results and common findings.
Non-linear EEG features for odor pleasantness recognition

Since olfactory sense is gaining ground in multimedia applications, it is important to understand the way odor pleasantness is perceived. Although several studies have explored the way odor pleasantness perception influences the power spectral density of the electroencephalography (EEG) in various brain regions, there are still no studies that investigate the way odor pleasantness perception affects the non-linear temporal variations of EEG. In this study two non-linear metrics are used, namely permutation entropy, and dimension of minimal covers, to explore the possibility of recognizing odor pleasantness perception from the non-linear properties of EEG signals. The results reveal that it is possible to discriminate between pleasant and unpleasant odors from the EEG nonlinear properties, using a Linear Discriminant Analysis classifier with cross-validation.
The emerging JPEG-2000 security (JPSEC) standard

The emergence of digital imaging applications is accelerating the need for security of digital imagery. The emerging international standard ISO/IEC JPEG-2000 security (JPSEC) is designed to provide security for digital imagery, and in particular digital imagery coded with the JPEG-2000 image coding standard. This paper provides an overview of the JPSEC standard, including a description of its basic architecture and examples of its use
Classification of EEG signals using Dempster Shafer theory and a k-nearest neighbor classifier

A brain computer interface (BCI) is a communication system, which translates brain activity into commands for a computer or other devices. Nearly all BCIs contain as a core component a classification algorithm, which is employed to discriminate different brain activities using previously recorded examples of brain activity. In this paper, we study the classification accuracy achievable with a k-nearest neighbor (KNN) method based on Dempster-Shafer theory. To extract features from the electroencephalogram (EEG), autoregressive (AR) models and wavelet decomposition are used. To test the classification method an EEG dataset containing signals recorded during the performance of five different mental tasks is used. We show that the Dempster-Shafer KNN classifier achieves a higher correct classification rate than the classical voting KNN classifier and the distance-weighted KNN classifier.
Omnidirectional object duplicate detection

In this paper, we extend a graph-based approach for omnidirectional object duplicate detection in still images. Objects are detected from several points of view with different distances. The goal of this work is to determine how many training images have to be taken and from which points of view in order to achieve a certain efficiency. Moreover, the performance of the algorithm is improved by automatically generated images, where the original training images are scaled and rotated in 3D space. Our experiments show that four training images are enough for 3D object duplicate detection from a planar view point and ten training images for omnidirectional detection.
The JPEG XR image coding standard [Standards in a Nutshell]

JPEG XR is the newest image coding standard from the JPEG committee. It primarily targets the representation of continuous-tone still images such as photographic images and achieves high image quality, on par with JPEG 2000, while requiring low computational resources and storage capacity. Moreover, it effectively addresses the needs of emerging high dynamic range imagery applications by including support for a wide range of image representation formats.
Annoyance of spatio-temporal artifacts in segmentation quality assessment [video sequences]

This paper describes the results of a series of subjective experiments that investigated the annoyance caused by the most common artifacts present in segmented video sequences. Various types of artifacts were inserted into a reference segmented video, considered as ideal, and shown to our test subjects. The artifacts varied in their location, size, appearance and duration. Annoyance of segmentation artifacts are found to be tied up with their intrinsic characteristics (e.g., size, position) but only weakly related to the video content. The results identify the characteristics that should be taken into account in the design of a perceptually driven objective metric.
Encoder and decoder side global and local motion estimation for Distributed Video Coding

In this paper, we propose a new Distributed Video Coding (DVC) architecture where motion estimation is performed both at the encoder and decoder, effectively combining global and local motion models. We show that the proposed approach improves significantly the quality of Side Information (SI), especially for sequences with complex motion patterns. In turn, it leads to rate-distortion gains of up to 1 dB when compared to the state-of-the-art DISCOVER DVC codec.
Using warping for privacy protection in video surveillance

The widespread use of digital video surveillance systems has also increased the concerns for violation of privacy rights. Since video surveillance systems are invasive, it is a challenge to find an acceptable balance between privacy of the public under surveillance and the functionalities of the systems. Tools for protection of visual privacy available today lack either all or some of the important properties such as security of protected visual data, reversibility (ability to undo privacy protection), simplicity, and independence from the video encoding used. In this paper, we propose an algorithm based on well-known warping techniques (common for animation and artistic purposes) to obfuscate faces in video surveillance, aiming to overcome these shortcomings. To demonstrate the feasibility of such an approach, we apply warping algorithm to faces in a standard Yale dataset and run face detection and recognition algorithms on the resulted images. Experiments demonstrate the tradeoff between warping strength and accuracy for both detection and recognition.
MPEG-based personalized content delivery

In this paper, we present a personalized multimedia content delivery system dealing with both user preferences and terminal/network capabilities. In order to ease interoperability with third-party applications, content annotation, user preferences handling and terminal/network capabilities description are managed by MPEG-7 and MPEG-21 standards. Offline content adaptation and annotation tools are proposed for content preparation. Content delivery is handled by client-server architecture. This architecture is built around custom personalization and adaptation tools, and a commercial content streamer. The proposed system has been used to provide universal multimedia access within the EC-funded R&D project PERSEO.
Perceptual quality assessment for video watermarking

The reliable evaluation of the performance of watermarking algorithms is difficult. An important aspect in this process is the assessment of the visibility of the watermark. We address this issue and propose a methodology for evaluating the visual quality of watermarked video. Using a software tool that measures different types of perceptual video artifacts, we determine the most relevant impairments and design the corresponding objective metrics. We demonstrate their performance through subjective experiments on several different watermarking algorithms and video sequences.