nils-herrmann/clean_scirepeval huggingface dataset:
PET image reconstruction with allowance for errors in the system model

In tomographic image reconstruction, it is usually assumed that the system matrix (describing the point source response) is known exactly. Here, the authors attempt to model the system matrix more realistically as the sum of a known part and an unknown random part that accounts for any errors. By using some simplifying assumptions, the authors develop a penalized weighted least squares (PWLS) reconstruction algorithm for this problem. Their experiments indicate that this approach can, indeed, lead to significant improvements in the reconstructed image, both visually and quantitatively.
Motion compensated reconstruction of 4D SPECT using parallel computation and deformable content adaptive mesh

In this abstract, we present preliminary evaluation of a full 4D reconstruction method aimed at cardiac gated emission tomography. The proposed method uses content adaptive mesh model (CAMM) to represent the 3D volumetric data. The nodes of the CAMM are then moved from a gate to gate, according to the motion obtained from a motion estimation algorithm, to form 4D deformable content adaptive mesh model (DCAMM). The use of a CAMM as well as DCAMM allows incorporating all data degradation models, namely objecting attenuation and detector-collimator spatial response, referred to as distance dependent blur. The parallel implementation, using OpenCL, was essential to shorten the needed computational time and make this a practical reconstruction methodology. In this work we successfully tested proposed parallel implementation by reconstructing images obtained from a realistic cardiac gated SPECT simulation using SIMIND Monte Carlo package and NCAT phantom.
Generalization Evaluation of Machine Learning Numerical Observers for Image Quality Assessment

In this paper, we present two new numerical observers (NO) based on machine learning for image quality assessment. The proposed NOs aim to predict human observer performance in a cardiac perfusion-defect detection task for single-photon emission computed tomography (SPECT) images. Human observer (HumO) studies are now considered to be the gold standard for task-based evaluation of medical images. However such studies are impractical for use in early stages of development for imaging devices and algorithms, because they require extensive involvement of trained human observers who must evaluate a large number of images.
How many reconstruction methods are needed for training a numerical observer?

In medical imaging it is now established that image quality should be evaluated using task-based criteria, such as human-observer (HO) performance in a medical decision task (e.g. lesion-detection). HO studies are usually costly and time consuming, therefore the development of a numerical observer (NO) surrogate, an algorithm that mimics HO, is highly desirable. Recently, we proposed and successfully tested a supervised-learning approach for modeling HO with a machine-learning algorithm (namely a support vector machine). In the supervised-learning approach, the goal is to identify the mapping (regression) between measured image features and defect likelihood scores given to an image by an HO. To identify this mapping (training phase), the proposed methodology uses a number of images for which human observer scores are available. The number of images and reconstruction methods for which the HO scores are available are limited. Therefore, in this work we are evaluating the proposed machine-learning based numerical observer performance as a function of the number of different reconstruction methods used during the training phase. The results indicate, as would be expected, that the more reconstruction methods used, the better the NO performance, but, surprisingly, the improvement of having more than five or six reconstruction methods is not significant.
Segmentation of dynamic PET or fMRI images based on a similarity metric

In this paper, we present a new approach for segmentation of image sequences by clustering the pixels according to their temporal behavior. The clustering metric we use is the normalized cross-correlation, also known as similarity. The main advantage of this metric is that, unlike the traditional Euclidean distance, it depends on the shape of the time signal rather than its amplitude. We model the intra-class variation among the time signals by a truncated exponential probability density distribution, and apply the expectation-maximization (EM) framework to derive two iterative clustering algorithms. Our numerical experiments using a simulated, dynamic PET brain study demonstrate that the proposed method achieves the best results when compared with several existing clustering methods.
Structural based side information creation with improved matching criteria for Wyner-Ziv video coding

The Wyner-Ziv video coding (WZVC) efficiency is highly dependent on the quality of the side information (SI) created at the decoder, typically through motion compensated frame interpolation (MCFI) techniques. Since the decoder only has available some reference decoded frames, SI creation turns out to be a difficult problem in WZVC. In most the MCFI techniques available, the matching criterion used for motion estimation only takes into account the (mean of absolute) pixel differences within a block which is limiting for some content types. This paper proposes a structural based SI creation approach with improved matching criteria combining local image features, obtained from the histogram of oriented gradients (HOG), with a boundary continuity criterion and the typical pixel differences criterion. With the structural based SI creation, the motion estimation process becomes more robust, e.g. to illumination changes, thus improving the SI quality (up to 0.4 dB) and reducing the bitrate (up to 6 % in terms of the Bjontegaard metric) regarding a state-of-the-art MCFI solution.
Coding binary local features extracted from video sequences

Local features represent a powerful tool which is exploited in several applications such as visual search, object recognition and tracking, etc. In this context, binary descriptors provide an efficient alternative to real-valued descriptors, due to low computational complexity, limited memory footprint and fast matching algorithms. The descriptor consists of a binary vector, in which each bit is the result of a pairwise comparison between smoothed pixel intensities. In several cases, visual features need to be transmitted over a bandwidth-limited network. To this end, it is useful to compress the descriptor to reduce the required rate, while attaining a target accuracy for the task at hand. The past literature thoroughly addressed the problem of coding visual features extracted from still images and, only very recently, the problem of coding real-valued features (e.g., SIFT, SURF) extracted from video sequences. In this paper we propose a coding architecture specifically designed for binary local features extracted from video content. We exploit both spatial and temporal redundancy by means of intra-frame and inter-frame coding modes, showing that significant coding gains can be attained for a target level of accuracy of the visual analysis task.
SIFT-Based Homographies for Efficient Multiview Distributed Visual Sensing

Considering the scarce resources in visual sensor networks, video coding solutions are necessary to address these constraints, namely the limited energy, memory, and bandwidth. The distributed video coding (DVC) paradigm is able to address these limitations by shifting most of the complexity to the decoder, typically a central location with a significant amount of resources. In a DVC context, the video codec rate-distortion (RD) performance is strongly dependent on the efficient generation of the so-called side information (SI). While in monoview DVC solutions the SI is generated by exploiting only the temporal correlation between frames, in multiview DVC (MV-DVC) solutions, the SI may be estimated by exploiting both the temporal and interview correlations. In this paper, a novel MV-DVC solution is proposed to improve the state-of-the-art MV-DVC RD performance with two key contributions in terms of an interview SI creation and SI fusion. According to the obtained experimental results, the proposed solution improves the RD performance associated to the current MV-DVC state-of-the-art solutions, exhibiting bitrate savings $\sim 10$ % for the same output video quality, without increasing the encoding complexity and eventually also the decoding complexity.
Multi-view distributed source coding of binary features for visual sensor networks

Visual analysis algorithms have been mostly developed for a centralized scenario where all visual data is acquired and processed at a central location. However, in visual sensor networks (VSN), several constraints in computational power, energy and bandwidth require a radically different approach, notably a paradigm shift from centralized to distributed visual processing. In the new paradigm, visual data is acquired and features are extracted at the sensing nodes locations to be after transmitted to enable further analysis at some central location. In such scenario, one of the key challenges is to design suitable feature coding schemes that are able to exploit the correlation among the features corresponding to (partially) overlapped views of the same visual scene. To achieve efficient coding, it is proposed to employ the distributed source coding paradigm as it does not require any communication between the sensing nodes (rather expensive in VSN) and it is parsimonious in terms of computational resources. Experimental results show that significant accuracy and compression gains (up to 37.36%) can be achieved when coding features extracted from multiple views.
Rate-Accuracy Optimization of Deep Convolutional Neural Network Models

Recently, deep learning has enjoyed a great deal of success for computer vision problems due to its capability to model highly complex tasks, such as image classification, object detection, face recognition, among many others. Although these neural networks are nowadays very powerful, there is a huge amount of parameters (i.e. the model) that need to be learned and require considerable storage space and bandwidth during transmission. This paper addresses the problems of storage and transmission of large deep learning models by proposing a compression solution that is independent of the model being trained as well as the data used for training. An efficient compression framework for the parameters of a neural network, more precisely the weights that interconnect the different neurons, which consume a significant amount of resources (memory, storage and bandwidth) is proposed. Several quantization strategies are considered as well as a statistical models for the different layers of a neural network, which are exploited by an arithmetic coding engine. Experimental results show that up to 92% bitrate savings can be obtained with minimal impact in terms of image classification accuracy.
Distributed Video Coding with multiple side information

Distributed Video Coding (DVC) is a new video coding paradigm which mainly exploits the source statistics at the decoder based on the availability of some decoder side information. The quality of the side information has a major impact on the DVC Rate-Distortion (RD) performance in the same way the quality of the predictions had a major impact in predictive video coding. In this paper, a DVC solution exploiting multiple side information is proposed; the multiple side information is generated by frame interpolation and frame extrapolation targeting to improve the side information of a single estimation mode. Compared with the best available single side information solutions, the proposed DVC solution with multiple side information robustly improves the RD performance for the set of test sequences.
Saliency-driven omnidirectional imaging adaptive coding: Modeling and assessment

Omnidirectional imaging, also known as 360° and spherical imaging, records all 360° of a scene from a specific spatial position, thus offering the user the capability to enjoy three rotational degrees of freedom (3-DoF). To offer a good quality of experience, omnidirectional imaging requires very high bitrates as high spatial resolution are a must and, ideally, also high frame rates. Due to the lack of video coding solutions specifically designed for omnidirectional imaging, this type of content is typically coded with the available image and video coding standards, such as JPEG, H.264/AVC and HEVC, after applying a 2D rectangular projection. In this context, this paper proposes an omnidirectional imaging coding solution allowing to reach improved coding performance by using an adaptive coding solution where the most visually salient image/video regions are coded with higher quality in a process appropriately controlled by the quantization parameter. To determine the saliency of the various omnidirectional imaging regions, a machine-learning based saliency detection model is proposed. The proposed coding solution achieves compression gains as measured by a novel objective quality metric also driven by saliency. This novel objective quality metric is validated by formal subjective testing where very high correlations with the subjective tests scores are achieved.
Multi-view distributed coding and selection of local binary features

Recently, the latest advances in compact feature representation and feature learning have provided an efficient framework for several visual analysis tasks, such as object recognition. However, when multiple cameras with overlapping fields-of-view are employed, other visual analysis tasks such as depth estimation can be supported and object recognition accuracy can be improved. In this paper the problem of distributed visual analysis from multiple views of a scene is addressed, considering that computational power and bandwidth, at each camera sensor, are rather limited. More specifically, an efficient coding technique for local binary features is proposed which exploits the correlation at the decoder side between each descriptor and its quantized representation. Moreover, considering that descriptors representing the same visual feature across different views are well correlated, a technique to avoid the transmission of redundant descriptors from multiple views is proposed. At the decoder, the joint statistics of all descriptors from all views is used to drive the selection of the best descriptors to be transmitted by each sensing node. The proposed multi-view feature coding and selection techniques allow obtaining bitrate reductions up to 80%, with respect to the uncompressed descriptor rate, for a certain task accuracy.
Subjective and objective quality evaluation of 3D point cloud denoising algorithms

The increasing availability of point cloud data in recent years is demanding for high performance denoising methods and compression schemes. When point cloud data is directly obtained from depth sensors or extracted from images acquired from different viewpoints, imprecisions on the depth acquisition or in the 3D reconstruction techniques result in noisy point clouds which may include a significant number of outliers. Moreover, the quality assessment of point clouds is a challenging problem since this 3D representation format is unstructured and it is typically not directly visualized. In this paper, selected objective quality metrics are evaluated regarding their correlation with human quality assessment and thus human perception. As far as the authors know, this is the first paper performing the subjective assessment of point cloud denoising algorithms and the evaluation of most used point cloud objective quality metrics. Experimental results show that graph-based denoising algorithms can improve significantly the point cloud quality data and that objective metrics that model the underlying point cloud surface can correlate better with human perception.
H.264/AVC backward compatible bit-depth scalable video coding

As high dynamic range video is gaining popularity, video coding solutions able to efficiently provide both low and high dynamic range video, notably with a single bitstream, are increasingly important. While simulcasting can provide both dynamic range videos at the cost of some compression efficiency penalty, bit-depth scalable video coding can provide a better trade-off between compression efficiency, adaptation flexibility and computational complexity. Considering the widespread use of H.264/AVC video, this paper proposes a H.264/AVC backward compatible bit-depth scalable video coding solution offering a low dynamic range base layer and two high dynamic range enhancement layers with different qualities, at low complexity. Experimental results show that the proposed solution has an acceptable rate-distortion performance penalty regarding the HDR H.264/AVC single-layer coding solution.
Augmented LDPC graph for distributed video coding with multiple side information

The advances made in channel-capacity codes, such as turbo codes and low-density parity-check (LDPC) codes, have played a major role in the emerging distributed source coding paradigm. LDPC codes can be easily adapted to new source coding strategies due to their natural representation as bipartite graphs and the use of quasi-optimal decoding algorithms, such as belief propagation. This paper tackles a relevant scenario in distributed video coding: lossy source coding when multiple side information (SI) hypotheses are available at the decoder, each one correlated with the source according to different correlation noise channels. Thus, it is proposed to exploit multiple SI hypotheses through an efficient joint decoding technique with multiple LDPC syndrome decoders that exchange information to obtain coding efficiency improvements. At the decoder side, the multiple SI hypotheses are created with motion compensated frame interpolation and fused together in a novel iterative LDPC based Slepian-Wolf decoding algorithm. With the creation of multiple SI hypotheses and the proposed decoding algorithm, bitrate savings up to 8.0% are obtained for similar decoded quality.
Design and performance of a novel low-density parity-check code for distributed video coding

Low-density parity-check (LDPC) codes are nowadays one of the hottest topics in coding theory, notably due to their advantages in terms of bit error rate performance and low complexity. In order to exploit the potential of the Wyner-Ziv coding paradigm, practical distributed video coding (DVC) schemes should use powerful error correcting codes with near-capacity performance. In this paper, new ways to design LDPC codes for the DVC paradigm are proposed and studied. The new LDPC solutions rely on merging parity-check nodes, which corresponds to reduce the number of rows in the parity-check matrix. This allows to change gracefully the compression ratio of the source (DCT coefficient bitplane) according to the correlation between the original and the side information. The proposed LDPC codes reach a good performance for a wide range of source correlations and achieve a better RD performance when compared to the popular turbo codes.
Epipolar based light field key-location detector

Nowadays, visual features play a key role, as they can provide a concise representation of visual data that is efficient for multiple tasks, notably content retrieval and object recognition. In parallel, visual sensors have been improving, targeting richer acquisitions of the light in a visual scene. In this context, the so-called light field cameras, which have recently emerged, are able to go beyond the standard acquisition models, by enriching the visual representation with directional light measures for each pixel position, e.g. by using a so-called lenslet light field camera. At this stage, not much research has been made in the field of feature detection and description for the emerging lenslet light field format. In this context, this paper proposes a feature detector suitable for lenslet light field images based on the exploitation of an alternative visual parametrization of the light field, called the Epipolar Planar Image (EPI). The proposed detector is heavily based on line detection in the EPI representation, since 3D points in the visual scene are mapped to line segments in an EPI, and the detector output is referred as key-locations. The proposed light field key-location detector is assessed with a solid evaluation framework using a large light field dataset. In comparison to the 2D SIFT detector, up to 10% improvements were achieved for the widely used repeatability metric.
Learning based decoding approach for improved Wyner-Ziv video coding

Wyner-Ziv (WZ) video coding compression efficiency depends critically both on the side information (SI) quality and the correlation noise model (CNM) accuracy. In this context, this paper proposes a learning based decoding approach for transform domain WZ video coding, notably in the context of the following techniques: i) fractional-pixel motion field learning to define the relevance of the SI block candidates, and ii) CNM parameter learning. Experimental results show the proposed learning approach brings consistent RD performance improvements, with coding gains up to 3.9 dB regarding the state-of-the-art DISCOVER WZ video codec for a GOP size of 8.
Optimal reconstruction for a HEVC backward compatible distributed scalable video codec

In a landscape of heterogeneous networks, terminals and usage environments, a low encoding complexity scalable video coding engine is required for many emerging applications such as wireless video surveillance, visual sensor networks and remote space transmission. To fulfil this need, a distributed scalable video coding (DSVC) framework has been developed combining the predictive and distributed coding paradigms. The DSVC framework provides High Efficiency Video Coding (HEVC) backward compatibility at the base layer (BL) and adopts a distributed coding approach for the enhancement layers (ELs). In DSVC, the decoder reconstruction plays a key role as it strongly impacts the EL decoded frame quality and, thus, the final DSVC compression efficiency. In this context, this paper proposes a statistically inspired decoder reconstruction solution for the EL coded information based on the so-called side information residue and its correlation with the encoder EL residue. Experimental results confirm that significant RD performance gains can be achieved with the proposed statistical reconstruction technique, notably up to 11.28% BD-Rate savings regarding the most relevant benchmark.
Packet-header based no-reference quality metrics for H.264/AVC video transmission

In video streaming, bitstreams are often transmitted in best-effort IP networks where impairments such as congestion and varying delay often cause artifacts in the decoded video. In this scenario, packet losses should be detected as early as possible in the transmission chain, preferably inside the network, where perceptual video quality metrics are needed to assess the users Quality of Experience (QoE). These metrics should have high efficiency and low complexity, especially to satisfy the demanding real-time and scalability requirements (many video bitstreams) of the network nodes. This paper proposes low complexity no-reference video quality metrics for H.264/AVC video transmissions in packet-based networks. These video quality metrics predict subjective scores that represent well the decoded quality with features extracted from the headers that encapsulate compressed video data. The evaluation shows that packet-header features allow an accurate quality prediction when packet losses occur in video transmission.