nils-herrmann/clean_scirepeval huggingface dataset:
3-D Position and Amplitude VLC Coding in H.264/AVC

Hybrid variable length coding (HVLC) was recently proposed as a novel entropy coding scheme for block-based image and video compression, which divides each transform block into low frequency (LF) region and high frequency (HF) region and codes them differently. To take advantage of the clustered nature of the nonzero coefficients in the LF region, a two-dimensional position and one-dimensional amplitude coding scheme (2DP1DA) was also proposed, which codes the 2D position information, i.e., run of consecutive zero-valued coefficients and run of consecutive nonzero coefficients, jointly by a 2D code table. To further improve the coding efficiency, joint position and amplitude coding is desirable. In this paper, we propose a three-dimensional position and amplitude coding scheme (3DPA) to jointly code the position and amplitude information for each nonzero cluster by a 3D code table. The experimental results show that HVLC with 3DPA for coding LF region achieves about 3.2%~3.7% bit rate reduction for a wide range of quantization parameters (QP) compared with CAVLC in H.264. Moreover, the 3DPA scheme is a compact way to realize the joint position and amplitude coding and it is very suitable for context-adaptive HVLC design.
A comparative study of quality and content-based spatial pooling strategies in image quality assessment

The process of quantifying image quality consists of engineering the quality features and pooling these features to obtain a value or a map. There has been a significant research interest in designing the quality features but pooling is usually overlooked compared to feature design. In this work, we compare the state of the art quality and content-based spatial pooling strategies and show that although features are the key in any image quality assessment, pooling also matters. We also propose a quality-based spatial pooling strategy that is based on linearly weighted percentile pooling (WPP). Pooling strategies are analyzed for squared error, SSIM and PerSIM in LIVE, multiply distorted LIVE and TID2013 image databases.
Network Lifetime Maximization for Estimation in Multihop Wireless Sensor Networks

We consider the distributed estimation by a network consisting of a fusion center and a set of sensor nodes, where the goal is to maximize the network lifetime, defined as the estimation task cycles accomplished before the network becomes nonfunctional. In energy-limited wireless sensor networks, both local quantization and multihop transmission are essential to save transmission energy and thus prolong the network lifetime. The network lifetime optimization problem includes three components: i) optimizing source coding at each sensor node, ii) optimizing source throughput of each sensor node, and iii) optimizing multihop routing path. Fortunately, source coding optimization can be decoupled from source throughput and multihop routing path optimization, and is solved by introducing a concept of equivalent 1-bit MSE function. Based on the optimal source coding, the source throughput and multihop routing path optimization is formulated as a linear programming (LP) problem, which suggests a new notion of character-based routing. The proposed algorithm is optimal and the simulation results show that a significant gain is achieved by the proposed algorithm compared with heuristic methods.
PODs:Partially Ordered Delivery for 3D Scenes in Resource-Constrained Environments

Three-dimensional (3D) graphic scenes require considerable network bandwidth to be transmitted and computing power to be rendered on users' terminals. Toward high-quality display in real time, we propose a sender-driven mechanism for streaming 3D scenes in a resource-constrained environment. By pre-processing the database, objects in the scene are properly weighted upon their rendering importance, and their resolutions are selected accordingly to reduce the bit rate. Partially ordered delivery is then performed using decoding independencies between the objects. Simulation results show the efficacy of the proposed mechanism. For a test benchmark, for example, the proposed algorithm outperforms the comparing heuristic by 4 dB under a 100-KB bit rate
Multi-Streaming of Visual Scenes with Scalable Partial Reliability

Three-dimensional (3D) visual scenes with pluralities of graphic objects require considerable network bandwidth to be transmitted and computing power to be rendered on a user's terminal. This paper proposes an application and transport cross-layer solution for efficiently streaming 3D scenes toward high-quality display. In so doing, 3D objects are scalably coded and are transmitted over multiple streams in a partially sequenced and partially reliable fashion. A rate-distortion optimization framework is developed to determine the optimal level of reliability for every chunk of data in each stream. Compared to heuristical methods, simulation results show that the proposed framework maximizes the display quality of the scene while minimizing the amount of data that needs to be processed by the client's rendering engine.
PerSIM: Multi-resolution image quality assessment in the perceptually uniform color domain

An average observer perceives the world in color instead of black and white. Moreover, the visual system focuses on structures and segments instead of individual pixels. Based on these observations, we propose a full reference objective image quality metric modeling visual system characteristics and chroma similarity in the perceptually uniform color domain (Lab). Laplacian of Gaussian features are obtained in the L channel to model the retinal ganglion cells in human visual system and color similarity is calculated over the a and b channels. In the proposed perceptual similarity index (PerSIM), a multi-resolution approach is followed to mimic the hierarchical nature of human visual system. LIVE and TID2013 databases are used in the validation and PerSIM outperforms all the compared metrics in the overall databases in terms of ranking, monotonic behavior and linearity.
Weakly-supervised labeling of seismic volumes using reference exemplars

Localizing seismic structures that can form traps for hydrocarbon reservoirs within large seismic volumes is a very challenging task. Due to the lack of accurately labeled data, we propose a weakly-supervised model for labeling seismic volumes using only a few labeled exemplars. Using six manually-labeled patches, we are able to extract patches that contain instances of similar geophysical structures. Features based on the effective singular values of curvelet coefficients are then used to train a classifier that can label an entire seismic volume with relatively high accuracy. Experiments on reference seismic sections in the Netherlands North Sea seismic dataset result in 73.8% mean pixel accuracy and 75.5% mean class accuracy, with an average labeling time of 5.2 seconds per section. These results are promising considering the nature of seismic images, and the lack of accurate edges between different geological structures.
Unsupervised estimation of uncertainty for video saliency detection using temporal cues

Video saliency detection is typically performed by combining temporal saliency and spatial saliency, which are detected separately. Among various available techniques, uncertainty-based combination is a unique and promising approach. In this paper, we study the uncertainty of each point within a map generated from video saliency detection. We develop an adaptive temporal correlation-based method for unsupervised uncertainty estimation. To evaluate the performance of our algorithm, we propose a systematic evaluation scheme that involves both the creation of ground truth uncertainty data and the comparison of uncertainty estimation results against the ground truth. Our experiments on the public CRCNS database show that the unsupervised uncertainty analysis algorithm is very promising.
An unequal error protection method for progressively transmitted 3D models

In this paper, we present a packet-loss resilient system for the transmission of progressively compressed three-dimensional (3D) models. It is based on a joint source and channel coding approach that trades off geometry precision for increased error resiliency to optimize the decoded model quality on the client side. We derive a theoretical framework for the overall system by which the channel packet loss behavior and the channel bandwidth can be directly related to the decoded model quality at the receiver. First, the 3D model is progressively compressed into a base mesh and a number of refinement layers. Then, we assign optimal forward error correction code rates to protect these layers according to their importance to the decoded model quality. Experimental results show that with the proposed unequal error protection approach, the decoded model quality degrades more gracefully (compared to either no error protection or equal error protection methods) as the packet-loss rate increases.
Content-adaptive non-parametric texture similarity measure

In this paper, we introduce a non-parametric texture similarity measure based on the singular value decomposition of the curvelet coefficients followed by a content-based truncation of the singular values. This measure focuses on images with repeating structures and directional content such as those found in natural texture images. Such textural content is critical for image perception and its similarity plays a vital role in various computer vision applications. In this paper, we evaluate the effectiveness of the proposed measure using a retrieval experiment. The proposed measure outperforms the state-of-the-art texture similarity metrics on CUReT and PerTex texture databases, respectively.
Feature Processing and Modeling for 6D Motion Gesture Recognition

A 6D motion gesture is represented by a 3D spatial trajectory and augmented by another three dimensions of orientation. Using different tracking technologies, the motion can be tracked explicitly with the position and orientation or implicitly with the acceleration and angular speed. In this work, we address the problem of motion gesture recognition for command-and-control applications. Our main contribution is to investigate the relative effectiveness of various feature dimensions for motion gesture recognition in both user-dependent and user-independent cases. We introduce a statistical feature-based classifier as the baseline and propose an HMM-based recognizer, which offers more flexibility in feature selection and achieves better performance in recognition accuracy than the baseline system. Our motion gesture database which contains both explicit and implicit motion information allows us to compare the recognition performance of different tracking signals on a common ground. This study also gives an insight into the attainable recognition rate with different tracking devices, which is valuable for the system designer to choose the proper tracking technology.
Unsupervised uncertainty analysis for video saliency detection

This paper presents a new unsupervised uncertainty estimation method for video saliency detection using spatial cues of the saliency map. The algorithm exploits the relationship between a pixel and its spatial neighbours in saliency maps to estimate the uncertainty of the saliency detected at the pixel location. Unlike supervised methods that fits uncertainty model to available training data, the proposed algorithm is based on very simple observation of the eye fixation map, which is largely influenced by human visual attention mechanisms. Thus, the proposed method is data independent. The performance of the proposed algorithm is evaluated using the challenging CRCNS video dataset and quantified using Receiver Operating Characteristics (ROC). The results are promising and could lead to robust uncertainty estimation using eye-fixation neighbourhood modeling.
Perceptual video quality assessment: Spatiotemporal pooling strategies for different distortions and visual maps

In this paper, we investigate the challenge of distortion map feature selection and spatiotemporal pooling in perceptual video quality assessment (PVQA). We analyze three distortion maps representing different visual features spatially and temporally: squared error, local pixel-level SSIM, and absolute difference of optical flow magnitudes. We examine the performance of each of these maps with different spatial and temporal pooling strategies across three databases. We identify the most effective statistical pooling strategies spatially and temporally with respect to PVQA. We also show the most significant spatial and temporal features correlated with perception for every distortion/feature map. Our results show that varying the pooling strategy and distortion maps yields a significant improvement in perceptual quality estimation. We also deduce insights from our results to better understand the sensitivity of human vision to distortions. We aim for these findings to provide perceptual cues and guidelines to researchers during metric design, perceptual feature selection, HVS modeling and pooling selection/optimization. We further show that the same distortions across databases can yield different results in terms of PVQA evaluation and verification.
Automatic fault tracking across seismic volumes via tracking vectors

The identification of reservoir regions has a close relationship with the detection of faults in seismic volumes. However, only relying on human intervention, most fault detection algorithms are inefficient. In this paper, we present a new technique that automatically tracks faults across a 3D seismic volume. To implement automation, we propose a two-way fault line projection based on estimated tracking vectors. In the tracking process, projected fault lines are integrated into a synthesized line as the tracked fault line, through an optimization process with local geological constraints. The tracking algorithm is evaluated using real-world seismic data sets with promising results. The proposed method provides comparable accuracy to the detection of faults explicitly in every seismic section, and it also reduces computational complexity.
ReSIFT: Reliability-weighted sift-based image quality assessment

This paper presents a full-reference image quality estimator based on SIFT descriptor matching over reliability-weighted feature maps. Reliability assignment includes a smoothing operation, a transformation to perceptual color domain, a local normalization stage, and a spectral residual computation with global normalization. The proposed method ReSIFT is tested on the LIVE and the LIVE Multiply Distorted databases and compared with 11 state-of-the-art full-reference quality estimators. In terms of the Pearson and the Spearman correlation, ReSIFT is the best performing quality estimator in the overall databases. Moreover, ReSIFT is the best performing quality estimator in at least one distortion group in compression, noise, and blur category.
Searching for the optimal curvelet tiling

Curvelets were recently introduced as a popular extension of wavelets. In the curvelet domain the input image is represented by sets of coefficients representing signal energy in different scales and angular directions. In this paper an algorithm that searches for optimal tilings for use with the curvelet transform is introduced. We consider two adaptations: scale locations, and the number of angular divisions per scale. A search algorithm that searches for the optimal tiling with respect to denoising performance is introduced. Results show significant improvement over original curvelet tilings. Tiling results were also tested with a seismic compressed sensing recovery problem. A similar performance advantage is reported.
Seismic interpretation of migrated data using edge-based geodesic active contours

Over the last few years, industry and academia have been shifting towards computer aided models for seismic interpretation because manual labeling and salt dome delineation is becoming very time consuming and labor intensive as the volume of seismic data is in the order of tera-bytes. In this paper, we have proposed an edge-based active contour with an arc length penalty using level set implementation for interpreter-assisted salt dome segmentation. We have designed an edge function and studied the effect of varying different active contour parameters on salt dome boundary detection. Experimental results show the effectiveness of proposed method on the real seismic dataset of F3 block in the North Sea. We have also compared our results with other state of the art methods of salt dome detection and the ground truth which show that active contours are very good candidate for segmenting salt dome.
Tensor-based subspace learning for tracking salt-dome boundaries

The exploration of petroleum reservoirs has a close relationship with the identification of salt domes. To efficiently interpret salt-dome structures, in this paper, we propose a method that tracks salt-dome boundaries through seismic volumes using a tensor-based subspace learning algorithm. We build texture tensors by classifying image patches acquired along the boundary regions of seismic sections and contrast maps. With features extracted from the subspaces of texture tensors, we can identify tracked points in neighboring sections and label salt-dome boundaries by optimally connecting these points. Experimental results show that the proposed method outperforms the state-of-the-art salt-dome detection method by employing texture information and tensor-based analysis.
Maximizing Network Lifetime for Estimation in Multi-Hop Wireless Sensor Networks

In this paper, we consider distributed estimation in energy-limited wireless sensor networks from lifetime-distortion perspective, where the goal is to maximize the network lifetime for a given distortion requirement. To take into account both local quantization and multi-hop transmission, which are essential to save transmission energy and thus prolong the network lifetime, the network lifetime maximization problem is formulated as a nonlinear programming (NLP) problem, where there are three factors needed to be optimized jointly: (i) source coding at each sensor, (ii) source throughput of each sensor, and (iii) multi-hop routing path. Furthermore, we show that this NLP problem can be decoupled without loss of optimality and reformulated as a linear programming (LP) problem. The proposed algorithm is optimal and the simulation results show that a significant gain is achieved by the proposed algorithm compared with heuristic methods.
Latency-Minimized Delivery for Multi-Resolution Mesh Geometry

Three-dimensional (3D) meshes are used intensively in distributed graphics applications where model data is transmitted on demand to users, terminals and rendered for interactive manipulation. This paper presents a transmission system for such applications with an objective of minimizing the latency between the user input and the response. In particular, we represent 3D models by multiple resolutions to allow fast and scalable rendering, and provide them with unequal error protection and/or retransmission when sending the data over a lossy link. The transmission policies are determined adaptive to environment variables and in linear computation time. Simulation results show that the proposed transmission system achieves 20-30% reduction on delivering latency compared to the state-of-the-art approach in the literature
Parity-Object Embedded Streaming for Synthetic Graphics

This paper proposes an object-oriented approach for streaming synthetic graphic contents over a packet-erasure channel. Graphic objects such as geometric meshes and texture images are coded independently into scalable resolutions and are transmitted in respective sequences to preserve their manipulation independency during packet delivery. For error resilience, FEC codes are generated as "parity objects" parallel to graphic objects. Using parity objects, multiple graphic objects are protected concurrently while also preferentially, in regard to their estimated importance on the display quality. Both the optimization framework and operational algorithm are developed for the generation of parity objects, and simulation results are presented to show the efficacy of the proposed mechanism.
Air-Writing Recognition—Part II: Detection and Recognition of Writing Activity in Continuous Stream of Motion Data

Air-writing refers to writing of characters or words in the free space by hand or finger movements. We address air-writing recognition problems in two companion papers. Part 2 addresses detecting and recognizing air-writing activities that are embedded in a continuous motion trajectory without delimitation. Detection of intended writing activities among superfluous finger movements unrelated to letters or words presents a challenge that needs to be treated separately from the traditional problem of pattern recognition. We first present a dataset that contains a mixture of writing and nonwriting finger motions in each recording. The LEAP from Leap Motion is used for marker-free and glove-free finger tracking. We propose a window-based approach that automatically detects and extracts the air-writing event in a continuous stream of motion data, containing stray finger movements unrelated to writing. Consecutive writing events are converted into a writing segment. The recognition performance is further evaluated based on the detected writing segment. Our main contribution is to build an air-writing system encompassing both detection and recognition stages and to give insights into how the detected writing segments affect the recognition result. With leave-one-out cross validation, the proposed system achieves an overall segment error rate of 1.15% for word-based recognition and 9.84% for letter-based recognition.
Gadgets and Signal Processing: The 2010 International CES [In the Spotlight]

The city of Las Vegas is known for its massive casinos, the number and variety of entertainment shows, and by an almost-established tradition that brings to this city one of the largest trade shows in the world: the Inter - national Consumer Electronics Show (CES). Every January, Las Vegas becomes the center of attention of the consumer electronics industry. This year¿s show presented hundreds of products, featured exhibitions of 226 companies, and discussed and demonstrated the latest trends in consumer electronics.
Progressive streaming of textured 3D models over bandwidth-limited channels

The bitstream of a progressively encoded textured model consists of multiple refinement layers. Decoding each layer produces a model with a simplified mesh and a resolution-reduced texture. We have proposed a quality measure that captures the visual fidelity of the multi-resolution textured models (Tian, D.H. and AlRegib, G.,  2004, p.684-91, 2004). Based on that quality measure, we consider the problem of streaming progressively encoded textured models over a bandwidth-limited channel. We develop a bit-allocation algorithm that optimally packetizes the source bits in every transmitted data unit, such that the perceptual quality of the model displayed on the client's screen is maximized. Experimental results confirm the effectiveness of the proposed bit-allocation algorithm.