nils-herrmann/clean_scirepeval huggingface dataset:
On the Modeling of Motion in Wyner-Ziv Video Coding

In the past few years, a number of practical video coding schemes following distributed source coding principles have emerged. One of the main goals of distributed video coding (DVC) is to enable a flexible distribution of the computational complexity between the encoder and the decoder, while approaching the coding efficiency of conventional closed-loop motion-compensated predictive codecs. In this paper we perform a rate-distortion analysis of a well-known Wyner-Ziv architecture, while focusing our attention on the impact of the motion modeling that is used for generating the side information at the decoder. Our analysis is structured according to a Kalman filtering problem and it allows us to compare three different scenarios: motion estimation at the encoder; motion interpolation at the decoder; and motion extrapolation at the decoder.
Inpainting-Based camera anonymization

Over the years, the forensic community has developed a series of very accurate camera attribution algorithms enabling to detect which device has been used to acquire an image with outstanding results. Many of these methods are based on photo response non uniformity (PRNU) that allows tracing back a picture to the camera used to shoot it. However, when privacy is required, it would be desirable to anonymize photos, unlinking them from their specific device. This paper investigates a new and alternative approach to image anonymization task. The proposed method leverages image inpainting described as an inverse regularized problem, and does not need any priors about the PRNU to remove. Specifically, we show how PRNU pattern can be strongly attenuated by reconstructing each pixel of an image from its neighbors, only slightly affecting visual quality. Results confirm this approach as a viable alternative solution for image anonymization.
Denoising infrared structured light DIBR signals using 3D morphological operators

The advent of low cost infrared structured light cameras (such as MS Xbox Kinect sensors) has brought the need for effective denoising algorithms that improves the quality and the accuracy of the reconstructed 3D scene. The overall noise depends on diverse factors and leads to significant alterations on object borders in the depth signal. It is possible to exploit a color signal acquired by a standard RGB camera to correct these alterations and interpolate the missing values. The paper presents a denoising and interpolation strategy that adopts 3D morphological operators to smooth and regularize volumes generated from the structured light cameras. Experimental results show that both the number of valid points and the quality of the warped views improve with respect to other regularizing approaches.
Image phylogeny tree reconstruction based on region selection

Nowadays, everyone can download, edit and republish any picture on the web, thus contributing to the diffusion of near-duplicate (ND) images. In order to gain an interesting insight on the way NDs are distributed online, recent works have focused on the reconstruction of the image phylogeny tree (IPT), i.e., an acyclic graph describing the genealogical relationship between ND image pairs. IPT reconstruction methods typically leverage the possibility of reconstructing one image from another one only if they are in parent-child relationship. However, as estimating the possible parent-child transformation is computationally expensive, usually a limited set of global editing operations is considered (i.e., compression, geometric and colour transformations applied to the whole image). However, in a real-world scenario it is customary to edit images also using local operations (e.g., logo insertion, object removal, splicing, etc.), which hinder the possibility of correctly estimating the parent-child relationship. In this paper, we propose an algorithm for IPT reconstruction that deals with the presence of local editing operations.
Fast Tracing of Acoustic Beams and Paths Through Visibility Lookup

The beam tracing method can be used for the fast tracing of a large number of acoustic paths through a direct lookup of a special tree-like data structure (beam tree) that describes the iterated visibility information from one specific position. This structure describes the branching of bundles of rays (beams) as they encounter reflectors in their paths. For this reason, beam tracing is suitable for real-time acoustic rendering even when the receiver is moving. In this paper, we propose a novel technique that enables the fast tracing of a large number of acoustic beams through the iterative lookup of a special data structure that describes the global visibility between reflectors. The method enables the immediate generation of the beam tree corresponding to an arbitrary source location, which can then be used for path tracing through direct lookup. In practice, this technique generalizes the traditional beam-tracing method as it makes it suitable for real-time acoustic rendering not just when the receiver is moving but also when the source is moving. The method enables real-time modeling of acoustic propagation and real-time auralization in complex 2-D and 2-Dtimes1-D environments (e.g., vertical walls limited by horizontal floor and ceiling), which makes it suitable for applications of real-time virtual acoustics, immersive gaming, and advanced acoustic rendering. Some experimental results show the effectiveness of fast beam tracing with respect to the state of the art in acoustic beam tracing.
Coding Visual Features Extracted From Video Sequences

Visual features are successfully exploited in several applications (e.g., visual search, object recognition and tracking, etc.) due to their ability to efficiently represent image content. Several visual analysis tasks require features to be transmitted over a bandwidth-limited network, thus calling for coding techniques to reduce the required bit budget, while attaining a target level of efficiency. In this paper, we propose, for the first time, a coding architecture designed for local features (e.g., SIFT, SURF) extracted from video sequences. To achieve high coding efficiency, we exploit both spatial and temporal redundancy by means of intraframe and interframe coding modes. In addition, we propose a coding mode decision based on rate-distortion optimization. The proposed coding scheme can be conveniently adopted to implement the analyze-then-compress (ATC) paradigm in the context of visual sensor networks. That is, sets of visual features are extracted from video frames, encoded at remote nodes, and finally transmitted to a central controller that performs visual analysis. This is in contrast to the traditional compress-then-analyze (CTA) paradigm, in which video sequences acquired at a node are compressed and then sent to a central unit for further processing. In this paper, we compare these coding paradigms using metrics that are routinely adopted to evaluate the suitability of visual features in the context of content-based retrieval, object recognition, and tracking. Experimental results demonstrate that, thanks to the significant coding gains achieved by the proposed coding scheme, ATC outperforms CTA with respect to all evaluation metrics.
Audio watermarking based technologies for automatic identification of musical pieces in audiotracks

In this paper we propose a fast and robust watermarking scheme for digital audio signals that allows automatic identification of musical pieces transmitted in TV broadcasting programs. Usually watermark is used as a way for hiding information on digital media. The watermarked information may be used to allow copyright protection or user and media identification. In our application the watermark must be, obviously, imperceptible to the users to mantain 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 watermarks using only a minimum amount of the computation power of a modern PC. Simulation results obtained on a long set of subjective test prove the quality and the robustness of the proposed approach.
Improving action classification with volumetric data using 3D morphological operators

This work deals with the definition of a framework for interpreting, modeling and classifying sequences of body movements into a pre-defined vocabulary of actions. Starting from sequences of volumetric reconstructions of the actor pose in each frame, we split action recognition into three separated tasks. The first task is the representation of the four-dimensional patterns reconstructed from each sequence, the second task is the extraction of motion descriptors, and the third task is the classification into action classes. In particular, we extract the curve skeleton from the reconstructed volumes in order to underly the actor movements and to reduce the system dependence from the actor gender and the body shape. The proposed method increases the action recognition rate.
Unsupervised feature learning for Music Structural Analysis

Music Structural Analysis (MSA) algorithms analyze songs with the purpose of automatically retrieving their large-scale structure. They do so from a feature-based representation of the audio signal (e.g., MFCCs, chromagram), which is usually hand-designed for that specific application. In order to design a proper audio representation for MSA, we need to assess which musical properties are relevant for segmentation purposes (e.g., timbre, harmony); and we need to design signal processing strategies that can be used for capturing them. Deep learning techniques offer an alternative to this approach, as they are able to automatically find an abstract representation of the musical content. In this work we investigate their use in the task of Music Structural Analysis. In particular, we compare the performance of several state-of-the-art algorithms working with a collection of traditional descriptors and by descriptors that are extracted with a Deep Belief Network.
A fast level-set approach to 2D and 3D reconstruction from unorganized sample points

In this paper we propose a novel algorithm for the reconstruction of surfaces from sets of unorganized sample points, based on the temporal evolution of a volumetric function's level-set. The evolving front can be thought of as the surface that separates two different fluids that obey specific laws of fluid dynamics. One remarkable feature of this approach is its ability to model complex topologies thanks to a novel strategy that allows us to steer the front evolution using Voronoi surfaces in 3D space. Another remarkable feature of this algorithm is its computational efficiency, which proved to between one and two orders of magnitude better than traditional level-set approaches J.A. Sethian (1999).
Detectability-quality trade-off in JPEG counter-forensics

Removing JPEG quantization footprints from an image inevitably introduces artifacts and traces in the spatial domain. Recently, several robust methods have been proposed to detect footprints of counter-forensics and recover the image's compression history. In this paper we investigate the limitations of these detectors, by proposing an improved counter-forensic attack which adds a postprocessing denoising step besides dithering. We consider both a general-purpose denoising algorithm and one targeted to JPEG images. In the latter case, we show that this approach can successfully reduce the accuracy of detectors in the literature to that of a random decision. As a second contribution, we study the trade-off between the detectability of counter-forensics and quality of the tampered image, and show that the loss of quality is not sufficient for the analyst to use available no-reference quality assessment tools as an indicator of an attack.
Multi-camera parameter tracking

The quality of 3-D reconstructions with multi-camera acquisition systems is strongly influenced by the accuracy of the camera calibration procedure. In fact, when acquiring a long sequence of views, mechanical shocks, vibrations and thermal gradients could cause a significant drift of the camera parameters. The authors propose a method for tracking the camera parameters and, whenever possible, correcting them accordingly. This technique does not need any a priori knowledge or test objects to be positioned in the scene, as it exploits natural scene features. The approach is based on accurate detection, matching and back-projection of luminance corners and spots in the scene space. Such features are then tracked over time to detect unexpected parameter changes or drifts, and to apply corrections to them. Experimental results on real sequences are reported in order to prove the effectiveness of the proposed technique. It is shown that changes in the calibration parameter are correctly detected and, when this happens, the camera system can be re-calibrated with an accuracy that increases with the number of tracked feature points.
Multiple description video coding for scalable and robust transmission over IP

In this paper, we address the problem of video transmission over unreliable networks, such as the Internet, where packet losses occur. The most recent literature indicates multiple description (MD) as a promising coding approach to handle this issue. Moreover, it has been shown also how important the use of motion compensation prediction is in an MD-coding scheme. This paper proposes two architectures for multiple description video coding, both of them are based on the motion compensation prediction loop. The common characteristic of the two architectures is the use of a polyphase down-sampling technique to create the MDs and to introduce cross-redundancy among the descriptions. The first scheme, that we call drift-compensation multiple description video coder (DC-MDVC) appears very robust when used in an error-prone environment, but it can provide only two descriptions. The second architecture, called independent flow multiple description video coder (IF-MDVC), generates multiple sets of data before the motion compensation loop; in this case, there are no severe limitations in the selection of the number of descriptions used by the coder.
Fast in-band motion estimation with variable size block matching

In this paper we propose a fast motion estimation technique that works in the wavelet domain. The computational cost of the algorithm turns out to be proportional to the linear size of the search window instead of its area. We complete our proposal with a variable-size block-matching scheme in the wavelet domain. We integrated the motion estimation algorithm in a fully-scalable wavelet in-band prediction coder inspired by the IB-MCTF (in-band motion compensation temporal filtering) proposed in J.C Ye et al., (2003). Comparative tests prove that our coder provides the same quality level than IB-MCTF at a very reduced computational cost. Moreover, although our method turns out to match the performance of MCTF-EZBC (P. Chen, 2003) in terms of PSNR, it clearly outperforms it in terms of perceptual quality, as it is completely free from blocking artifacts.
Improved Bit Allocation in an Error-Resilient Scheme Based on Distributed Source Coding

In this work we propose an error-resilient scheme that allows enhancing the robustness of a video stream. Based on distributed source coding (DSC) principles, an auxiliary stream is sent in parallel to the main stream as a redundant representation of the sequence that is used to correct errors at the decoder, thus reducing the impact of drift. In order to perform an optimal bit allocation in the auxiliary stream, the encoder needs to compute a reliable estimate of the expected video distortion observed at the decoder side due to channel loss. This paper proposes an algorithm to calculate the expected distortion of decoded DCT-coefficients (dubbed EDDD) and its application to the bit allocation problem in a DSC based auxiliary stream
Egomotion estimation of a multicamera system through line correspondences

In this paper we propose a method for estimating the egomotion of a calibrated multi-camera system from an analysis of the luminance edges. The method works entirely in the 3D space as all edges of each one set of views are previously localized, matched and back-projected onto the object space. In fact, it searches for the rigid motion that best merges the sets of 3D contours extracted from each one of the multi-views. The method uses both straight and curved 3D contours.
Neural network approach for adaptive vector quantization

The problem of adaptive vector quantization (VQ) for image sequence coding is addressed. The goal of this work is to overcome the limits that reduce the possibility of a hardware implementation of the schemes already presented in the literature. The limits principally concern the complexity of the implementation in real time of the classical Linde-Buzo-Gray (LBG) algorithm, which is necessary to introduce some adaptive capabilities in a VQ. Neural network methods, because of their fast codebook design, seem an interesting alternative to solve this problem. The authors have used an unsupervised neural network approach to introduce adaptivity in a vector quantizer by using a codebook replenishment method. The proposed adaptive VQ algorithm has been tested in a motion compensated interframe image coding scheme. The results of the simulations are very promising. A considerable rise of the coder performance with respect to the use of fixed VQ has been obtained.<<ETX>>
Forensic and anti-forensic analysis of indoor/outdoor classifiers based on acoustic clues

This paper addresses the problem of identifying the class of the environment where an audio recording was taken. We focus on distinguishing between indoor and outdoor speech recordings, and we propose a set of classifiers that provide a support for the forensic analyst in verifying the authenticity of audio content. The classifiers rely on acoustic clues extracted from the reverberant signal, namely the reverberation time (RT60) and MFCC/LMSC feature vectors. We conducted several experiments, aimed at analyzing the algorithms from both the forensic and anti-forensic perspective. To do so, we devised a methodology for manipulating the signals in order to pretend that outdoor contents were recorded indoor, and vice-versa. Experimental results confirm the effectiveness of the proposed methods, which achieve high classification ac curacy. The anti-forensics analysis reveals that attacks have moderate success rates, and severely depend from the classi fication algorithm adopted by the analyst.
Who is my parent? Reconstructing video sequences from partially matching shots

Nowadays, a significant fraction of the available video content is created by reusing already existing online videos. In these cases, the source video is seldom reused as is. Conversely, it is typically time clipped to extract only a subset of the original frames, and other transformations are commonly applied (e.g., cropping, logo insertion, etc.). In this paper, we analyze a pool of videos related to the same event or topic. We propose a method that aims at automatically reconstructing the content of the original source videos, i.e., the parent sequences, by splicing together sets of near-duplicate shots seemingly extracted from the same parent sequence. The result of the analysis shows how content is reused, thus revealing the intent of content creators, and enables us to reconstruct a parent sequence also when it is no longer available online. In doing so, we make use of a robust-hash algorithm that allows us to detect whether groups of frames are near-duplicates. Based on that, we developed an algorithm to automatically find near-duplicate matchings between multiple parts of multiple sequences. All the near-duplicate parts are finally temporally aligned to reconstruct the parent sequence. The proposed method is validated with both synthetic and real world datasets downloaded from YouTube.
A Counter-Forensic Method for CNN-Based Camera Model Identification

An increasing number of digital images are being shared and accessed through websites, media, and social applications. Many of these images have been modified and are not authentic. Recent advances in the use of deep convolutional neural networks (CNNs) have facilitated the task of analyzing the veracity and authenticity of largely distributed image datasets. We examine in this paper the problem of identifying the camera model or type that was used to take an image and that can be spoofed. Due to the linear nature of CNNs and the high-dimensionality of images, neural networks are vulnerable to attacks with adversarial examples. These examples are imperceptibly different from correctly classified images but are misclassified with high confidence by CNNs. In this paper, we describe a counter-forensic method capable of subtly altering images to change their estimated camera model when they are analyzed by any CNN-based camera model detector. Our method can use both the Fast Gradient Sign Method (FGSM) or the Jacobian-based Saliency Map Attack (JSMA) to craft these adversarial images and does not require direct access to the CNN. Our results show that even advanced deep learning architectures trained to analyze images and obtain camera model information are still vulnerable to our proposed method.
3D motion estimation of a trinocular system for a full-3D object reconstruction

Motion estimation of a calibrated multi-ocular acquisition system can be performed either on two-dimensional data, by applying a rigidity constraint to a set of matched points on monocular views, or on three-dimensional data, by determining the rigid motion that best overlaps homologous 3D points obtained through stereo matching and back-projection. We propose, analyze and compare these two possible solutions, and present a low-cost high-accuracy full-3D reconstruction system based on multiple trinocular views at standard TV resolution.
Uncalibrated view synthesis from Relative Affine Structure based on planes parallelism

This paper focuses on the generation of physically valid views from two or more uncalibrated images acquired by standard cameras. The problem is faced without trying to yield a three dimensional reconstruction of the imaged scene, which would be unfeasible without the exact knowledge of the positions of the cameras in the Euclidean frame where the scene is to be described. Instead, starting from the previous works of Shashua and Navab on relative affine structure (1996) and the article of Fusiello on views synthesis from uncalibrated views (2007) we propose a novel approach that does not require the presence of a plane at infinity to define the homography between two views but merely the parallelism between couples of planes. This allows our approach to be applied to numerous scenes where two parallel planes can be defined (indoor scenes, straight streets and avenues). Experiments with synthetic images illustrate the approach.
Analysis of Coding Efficiency of Motion-Compensated Interpolation at the Decoder in Distributed Video Coding

This paper analyzes the coding efficiency of distributed video coding (DVC) schemes that perform motion-compensated interpolation at the decoder. The decoder has access only to the key frames when generating the side information for intermediate frames. This fact introduces a displacement estimation error that depends on several factors: 1) the overall motion complexity; 2) the temporal coherence of the motion field; 3) the temporal distance between successive key frames. Adopting a state-space model and a Kalman filtering framework, we obtain an estimate of the displacement error variance. This is used to determine the rate-distortion function of the overall coding scheme, that takes into account both intra-coded key frames and DVC-coded frames. The proposed model shows that motion-compensated interpolation is unable to achieve the coding efficiency of conventional motion-compensated predictive coding.