nils-herrmann/clean_scirepeval huggingface dataset:
Image coding with channel-optimized trellis-coded quantization for channels with memory

This paper presents a wavelet-based image coder that is optimized for transmission over binary channels with memory. The proposed coder uses a channel-optimized trellis-coded quantizer (COTCQ) designed for a binary first-order Markov channel. The quantizer stage exploits the channel memory by incorporating the characteristics of the additive correlated channel noise during the quantizer design and by using a new trellis structure. The performance of the proposed memory-optimized COTCQ (MCOTCQ) image coding system is presented for different bit error probabilities and noise correlation parameters. It is shown that the performance of the coder is significantly improved when the second-order statistics of the noise are taken into account at the quantizer design level.
Efficient perceptual-based spatially varying out-of-focus blur detection

This paper proposes a blur detection algorithm that is capable of detecting and quantifying the level of spatially-varying blur by integrating directional edge spread calculation, Just Noticeable Blur (JNB) and local probability summation. The proposed method generates a blur map indicating the relative amount of perceived local blurriness. We compare the proposed method with six other state-of-the-art blur detection methods. Experimental results show that the proposed method performs the best both visually and quantitatively.
Objective Video Quality Assessment Methods: A Classification, Review, and Performance Comparison

With the increasing demand for video-based applications, the reliable prediction of video quality has increased in importance. Numerous video quality assessment methods and metrics have been proposed over the past years with varying computational complexity and accuracy. In this paper, we introduce a classification scheme for full-reference and reduced-reference media-layer objective video quality assessment methods. Our classification scheme first classifies a method according to whether natural visual characteristics or perceptual (human visual system) characteristics are considered. We further subclassify natural visual characteristics methods into methods based on natural visual statistics or natural visual features. We subclassify perceptual characteristics methods into frequency or pixel-domain methods. According to our classification scheme, we comprehensively review and compare the media-layer objective video quality models for both standard resolution and high definition video. We find that the natural visual statistics based MultiScale-Structural SIMilarity index (MS-SSIM), the natural visual feature based Video Quality Metric (VQM), and the perceptual spatio-temporal frequency-domain based MOtion-based Video Integrity Evaluation (MOVIE) index give the best performance for the LIVE Video Quality Database.
An efficient embedded zerotree wavelet image codec based on intraband partitioning

Embedded zerotree wavelet (EZW) coding, introduced by J. M. Shapiro (1993) and improved in the SPIHT codec of A. Said and W.A. Pearlman (1996), is a very effective wavelet image coding method. One main concept in EZW and SPIHT is the exploitation of self-similarity across different scales of the image wavelet transform to effectively compress the image data. This paper presents an efficient and fully embedded zerotree wavelet image codec that achieves a comparable and even better performance than SPIHT by exploiting only the self-similarity within the scales (intraband self-similarity). The proposed algorithm results in a simpler implementation and in a more efficient representation of the ordering information than SPIHT. Coding results are presented to illustrate the performance of the proposed embedded wavelet image codec.
A no-reference perceptual image sharpness metric based on saliency-weighted foveal pooling

A no-reference perceptual sharpness quality metric, inspired by visual attention information, is presented for a better simulation of the Human Visual System (HVS) response to blur distortions. Saliency information about a scene is used to accentuate blur distortions around edges present in conspicuous areas and attenuate those distortions present in the rest of the image. Simulation results are presented to illustrate the performance of the proposed metric.
Reduced-Reference Quality Assessment Based on the Entropy of DWT Coefficients of Locally Weighted Gradient Magnitudes

Perceptual image quality assessment (IQA) attempts to use computational models to estimate the image quality in accordance with subjective evaluations. Reduced-reference IQA (RRIQA) methods make use of partial information or features extracted from the reference image for estimating the quality of distorted images. Finding a balance between the number of RR features and accuracy of the estimated image quality is essential and important in IQA. In this paper, we propose a training-free low-cost RRIQA method that requires a very small number of RR features (six RR features). The proposed RRIQA algorithm is based on the discrete wavelet transform (DWT) of locally weighted gradient magnitudes. We apply human visual system’s contrast sensitivity and neighborhood gradient information to weight the gradient magnitudes in a locally adaptive manner. The RR features are computed by measuring the entropy of each DWT subband, for each scale, and pooling the subband entropies along all orientations, resulting in <inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula> RR features (one average entropy per scale) for an <inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula>-level DWT. Extensive experiments performed on seven large-scale benchmark databases demonstrate that the proposed RRIQA method delivers highly competitive performance as compared with the state-of-the-art RRIQA models as well as full reference ones for both natural and texture images. The MATLAB source code of <italic>REDLOG</italic> and the evaluation results are publicly available online at <uri xlink:type="simple">https://http://lab.engineering.asu.edu/ivulab/software/redlog/</uri>.
Transform-domain distributed video coding with rate-distortion-based adaptive quantisation

This study presents a transform-domain distributed video coding (DVC) system with a rate-distortion (R-D)-based Adaptive QuanTisation (AQT) scheme. In the proposed system, the transform-domain Wyner-Ziv frame is divided into partitions and is adaptively quantised based on estimated local R-D characteristics for each partition. The R-D estimation is performed based on a correlation model between the original source information and the side information and can be applied at the decoder without adding complexity to the encoder. Coding results and comparisons with existing DVC schemes and with H.264/AVC interframe and intraframe coding are presented to illustrate the performance of the proposed system.
Video Texture and Motion based Modeling of Rate Variability-Distortion (VD) Curves of I, P, and B Frames

We examine the bit rate variability-distortion (VD) curve of I, P, and B frames of MPEG-4 VBR encoded video sequences. We show that the concave VD curve shape at high compression ratios or large quantization scales, is influenced by both the texture and the motion information. We use linear and quadratic models for the texture and motion bits statistics and devise accurate VD curve models. The model parameters are obtained from statistics that are estimated from two encodings. This work extends our previous work on modeling the VD curve, which has applications for optimal statistical multiplexing of VBR streaming video
Reduced-reference synthesized-texture quality assessment based on multi-scale spatial and statistical texture attributes

In this paper, we propose a reduced-reference (RR) objective quality assessment method that quantifies the perceived quality of synthesized textures. The proposed metric is based on measuring the granularity, regularity, and statistical attributes of the texture image. Furthermore, the proposed RR metric exhibits a significantly low overhead as compared to existing RR metrics by only requiring the transmission of 7 parameters as side information. Performance evaluations on two synthesized texture databases demonstrate that the proposed RR metric outperforms both full-reference (FR) and RR state-of-the-art quality metrics in predicting the perceived visual quality of the synthesized textures.
Traffic and Statistical Multiplexing Characterization of 3-D Video Representation Formats

The network transport of 3-D video, which contains two views of a video scene, poses significant challenges due to the increased video data, compared to a conventional single-view video. Addressing these challenges requires a thorough understanding of the traffic and multiplexing characteristics of the different representation formats of a 3-D video. We examine the average bitrate-distortion (RD) and bitrate variability-distortion characteristics of three main representation formats. Specifically, we compare multiview video (MV) representation and encoding, frame sequential (FS) representation, and side-by-side (SBS) representation, whereby conventional single-view encoding is employed for the FS and SBS representations. Our results for long 3-D videos in the full HD format indicate that the MV representation and encoding achieves the highest RD efficiency, while exhibiting the highest bitrate variabilities. We examine the impact of these bitrate variabilities on network transport through extensive statistical multiplexing simulations. We find that when multiplexing a small number of streams, the MV and FS representations require the same bandwidth. However, when multiplexing a large number of streams or smoothing traffic, the MV representation and encoding reduces the bandwidth requirement relative to the FS representation.
Attentive gesture recognition

This paper presents a novel method for static gesture recognition based on visual attention. Our proposed method makes use of a visual attention model to automatically select points that correspond to fixation points of the human eye. Gesture recognition is then performed using the determined visual attention fixation points. For this purpose, shape context descriptors are used to compare the sparse fixation points of gestures for classification. Simulation results are presented in order to illustrate the performance of the proposed perceptual-based attentive gesture recognition method. The proposed method not only helps in the development of more natural user-centric interactive interfaces but is also able to achieve a 96.42% classification accuracy on the Triesch database of hand postures, which is superior to other methods presented in the literature.
On the Performance of Autocorrelation Estimation Algorithms for fMRI Analysis

Pre-whitening of fMRI time-series is commonly performed to address temporal autocorrelations. The pre-whitening procedure requires knowledge of the spatially dependent autocorrelations, which in turn must be estimated from the observed data. The accuracy of the autocorrelation estimation algorithm is important because biased autocorrelation estimates result in biased test statistics, thereby increasing the expected false-positive and/or false-negative rates. Thus, a methodology for testing the accuracy of autocorrelation estimates and for assessing the performance of today's state-of-the-art autocorrelation estimation algorithms is needed. To address these problems, we propose an evaluation framework that tests for significant autocorrelation bias in the model residuals of a general linear model analysis. We apply the proposed testing framework to 18 pre-surgical fMRI mapping datasets from ten patients and compare the performance of popular fMRI autocorrelation estimation algorithms. We also identify five consistent spectral patterns representative of the encountered autocorrelation structures and show that they are well described by a second-order/two-pole model. We subsequently show that a nonregularized, second-order autoregressive model, AR(2), is sufficient for capturing the range of temporal autocorrelations found in the considered fMRI datasets. Finally, we explore the bias versus predictability tradeoff associated with regularization of the autocorrelation coefficients. We find that the increased bias from regularization outweighs any gains in predictability. Based on the obtained results, we expect that a second-order, nonregularized AR algorithm will provide the best performance in terms of producing white residuals and achieving the best possible tradeoff between maximizing predictability and minimizing bias for most fMRI datasets.
Adaptive image coding with perceptual distortion control

This paper presents a discrete cosine transform (DCT)-based locally adaptive perceptual image coder, which discriminates between image components based on their perceptual relevance for achieving increased performance in terms of quality and bit rate. The new coder uses a locally adaptive perceptual quantization scheme based on a tractable perceptual distortion metric. Our strategy is to exploit human visual masking properties by deriving visual masking thresholds in a locally adaptive fashion. The derived masking thresholds are used in controlling the quantization stage by adapting the quantizer reconstruction levels in order to meet the desired target perceptual distortion. The proposed coding scheme is flexible in that it can be easily extended to work with any subband-based decomposition in addition to block-based transform methods. Compared to existing perceptual coding methods, the proposed perceptual coding method exhibits superior performance in terms of bit rate and distortion control. Coding results are presented to illustrate the performance of the presented coding scheme.
A No-Reference Objective Image Sharpness Metric Based on Just-Noticeable Blur and Probability Summation

This work presents a perceptual-based no-reference objective image sharpness/blurriness metric by integrating the concept of just noticeable blur (JNB) into a probability summation model. Unlike existing objective no-reference image sharpness/blurriness metrics, the proposed metric is able to predict the relative amount of blurriness in images with different content. Results are provided to illustrate the performance of the proposed perceptual-based sharpness metric. These results show that the proposed sharpness metric correlates well with the perceived sharpness.
Super-resolution using a Wavelet-based Adaptive Wiener Filter

In this paper, a Wavelet-based Adaptive Wiener Filter (WAWF) super-resolution (SR) algorithm is presented. A redundant discrete dyadic wavelet transform (DDWT) is applied to the input sequence to classify the LR frames into subbands of similar contextual information. The similar subbands from each LR frame are registered using subpixel motion and merged on the same HR grid to form HR subbands of similar contextual and statistical information. Then an adaptive Wiener filter approach is applied locally to each subband in order to interpolate the missing information on the HR gird. The weights of the filter are designed using a parametric circularly symmetric statistical model that adapts to the statistics and the spatial proximity of the neighboring wavelet coefficients. Simulation results show that the proposed WAWF SR algorithm results in a superior performance as compared to existing recent SR schemes.
Change detection on SAR images using divisive normalization-based image representation

In the context of multi-temporal synthetic aperture radar (SAR) images for earth monitoring applications, one critical issue is the detection of changes occurring after a natural or an-thropic disaster. In this paper, we propose a new similarity measure for automatic change detection based on a divisive normalization image representation. The divisive normalization transform (DNT) has been recognized as a successful methodology to model the perceptual sensitivity of biological vision and a useful image representation that significantly reduces statistical dependence of natural images. In this work, we exploit the fact that the histogram of DNT coefficients within wavelet subbands can often be well fitted with a zero-mean Gaussian density function, which is a one-parameter function that allows efficient change detection of SAR images. The proposed change detector is compared to other recent modelbased approaches. Tests on real data show that our detector outperforms previously suggested methods in terms of the rate of false alarm rate and the total error rate.
Computational Visual Attention Models

The human visual system (HVS) has evolved to have the ability to selectively focus on the most relevant parts of a visual scene. This mechanism, referred to as visual attention (VA), has been the focus of several neurological and psychological studies in the past few decades. These studies have inspired several computational VA models which have been successfully applied to problems in computer vision and robotics. In this paper we provide a comprehensive survey of the state-of-the-art in computational VA modeling with a special focus on the latest trends. We review several models published since 2012. We also discuss theoretical advantages and disadvantages of each approach. In addition, we describe existing methodologies to evaluate computational models through the use of eye-tracking data along with the VA performance metrics used. We also discuss shortcomings in existing approaches and describe approaches to overcome these shortcomings. A recent subjective evaluation for benchmarking existing VA metrics is also presented and open problems in VA are discussed. M. S. Gide and L. J. Karam Computational Visual Attention Models. Foundations and Trends  2016.
An Efficient Selective Perceptual-Based Super-Resolution Estimator

In this paper, a selective perceptual-based (SELP) framework is presented to reduce the complexity of popular super-resolution (SR) algorithms while maintaining the desired quality of the enhanced images/video. A perceptual human visual system model is proposed to compute local contrast sensitivity thresholds. The obtained thresholds are used to select which pixels are super-resolved based on the perceived visibility of local edges. Processing only a set of perceptually significant pixels reduces significantly the computational complexity of SR algorithms without losing the achievable visual quality. The proposed SELP framework is integrated into a maximum-a posteriori-based SR algorithm as well as a fast two-stage fusion-restoration SR estimator. Simulation results show a significant reduction on average in computational complexity with comparable signal-to-noise ratio gains and visual quality.
Improved foveation- and saliency-based visual attention prediction under a quality assessment task

Image quality assessment is one application out of many that can be aided by the use of computational saliency models. Existing visual saliency models have not been extensively tested under a quality assessment context. Also, these models are typically geared towards predicting saliency in non-distorted images. Recent work has also focussed on mimicking the human visual system in order to predict fixation points from saliency maps. One such technique (GAFFE) that uses foveation has been found to perform well for non-distorted images. This work extends the foveation framework by integrating it with saliency maps from well known saliency models. The performance of the foveated saliency models is evaluated based on a comparison with human ground-truth eye-tracking data. For comparison, the performance of the original non-foveated saliency predictions is also presented. It is shown that the integration of saliency models with a foveation based fixation finding framework significantly improves the prediction performance of existing saliency models over different distortion types. It is also found that the information maximization based saliency maps perform the best consistently over different distortion types and levels under this foveation based framework.
Error-resilient image coding and transmission over wireless channels

This paper introduces and implements a point-to-point error-resilient image coding and transmission scheme over a CDPD (cellular digital packet data) wireless channel, combining a low-delay UDP (user datagram protocol) transport protocol in conjunction with a channel-optimized wavelet-based image coder. The image coder uses a robust channel-optimized trellis-coded quantization (COTCQ) stage that is designed to optimize the image coding based on the channel characteristics. Packets are formed with the property that image information is spread over different frequency bands. The channel characterization is conducted in such a way so as to minimize the packet loss rate and maximize the net data throughput. Furthermore, a receiver-based rate control mechanism is presented at the application level to reduce significantly the image distortions and packet loss rate. Results are presented to illustrate the performance of the proposed system.
Locally adaptive perceptual quantization without side information for compression of visual data

This paper presents a locally adaptive perceptual quantization scheme for visual data compression. The strategy is to exploit human visual masking properties by deriving masking thresholds in a locally adaptive fashion based on a sub-band decomposition. The derived masking thresholds are used in controlling the quantization stage by adapting the quantizer reconstruction levels to the local amount of masking present at the level of each sub-band transform coefficient. Compared to the existing non-locally-adaptive perceptual quantization methods, the new locally adaptive algorithm exhibit superior performance and does not require additional side information. This is accomplished by estimating the amount of available masking from the already quantized data and linear prediction of the coefficient under consideration. By virtue of the local adaptation, the proposed quantization scheme is able to remove a large amount of perceptually redundant information. Since the algorithm does not require additional side information, it yields a low entropy representation of the image and is well suited for perceptually-lossless image compression.
An efficient, SELective, Perceptual-based super-resolution estimator

In this paper, a selective perceptual-based (SELP) scheme is presented to reduce the complexity of popular super-resolution (SR) algorithms while maintaining the desired quality of the enhanced images/video. A perceptual Human Visual System (HVS) model is proposed to compute the contrast sensitivity threshold for a given background intensity. The obtained thresholds are used to select which pixels are super-resolved based on the perceived visibility of local edges. This is accomplished by estimating the contrast sensitivity threshold locally over a block. Next, the absolute difference between each pixel and its neighbors is computed and compared to the threshold upon which a decision is made to include the pixel in the SR estimator for the next iteration or not. The perceptual model is integrated into a MAP-based SR algorithm as well as a fast ML estimator. Simulation results show up to 47% reduction on average in computational complexity with comparable SNR gains and visual quality.
A Captcha Based on the Human Visual Systems Masking Characteristics

In this paper, a CAPTCHA is presented based on the masking characteristics of the human visual system (HVS). Knowing that noise can be masked by high activity regions and showing that edges can be masked by noise for a human observer while still being detected by machines, the suggested CAPTCHA is composed of English alphabets that are picked randomly and written with a combination of texture and edges with added noise such as to deceive the machine by randomly changing the visibility of characters for humans. The proposed CAPTCHA is highly legible and robust to brute-force attacks and sophisticated object character recognition (OCR) segmentation algorithms