nils-herrmann/clean_scirepeval huggingface dataset:
An energy-efficient strategy for combined RSS-PDR indoor localization

We propose an optimization-based framework to minimize the energy consumption in a sensor network when using an indoor localization system based on the combination of received signal strength (RSS) and pedestrian dead reckoning (PDR). The objective is to find the RSS localization frequency and the number of RSS measurements used at each localization round that jointly minimize the total consumed energy, while ensuring at the same time a desired accuracy in the localization result. The optimization approach leverages practical models to predict the localization error and the overall energy consumption for combined RSS-PDR localization systems. The performance of the proposed strategy is assessed through simulation, showing energy savings with respect to other approaches while guaranteeing a target accuracy.
Rate-accuracy optimization in visual wireless sensor networks

We consider the problem of allocating the resources in a wireless sensor network, which is designed to perform visual analysis (e.g. object recognition). We depart from the traditional compress-then-analyze paradigm, in which nodes sense, compress and transmit visual data to a sink node. Instead, we study the case in which nodes extract and lossy code local features from pixel-domain representations of the sensed visual scene. The formulation of the allocation problem entails maximizing the lifetime of the visual sensor network subject to a target accuracy of the analysis task, together with energy, bandwidth and routing constraints. To this end, we contribute with the definition of a rate-accuracy model, which plays the role of the traditional rate-distortion model commonly adopted in visual communication. The proposed model captures the impact of: i) the number of selected local features; ii) the number of bits used for quantizing local features; iii) the criterion used to select the subset of local features to be transmitted. We verify the correctness of the models on two widely adopted visual dataset and we demonstrate the network lifetime gain that can be achieved by an optimal allocation of the resources.
Performance evaluation of object recognition tasks in visual sensor networks

We analyze the performance of TCP and UDP transport protocols when applied to image retrieval or object recognition in wireless visual sensor networks (VSN). We focus on two different paradigms for image analysis, namely compress-then-analyze (CTA) and analyze-then-compress (ATC). The former entails the transmission of JPEG encoded images from camera nodes to a server, where the analysis takes place. The latter consists in extracting and compressing local visual features on board camera nodes, before transmission to a remote location. The presented analysis is useful to assess the best coupling between application and transport layers under delay and accuracy constraints for different networking conditions.
Top-k pipe join

In the context of service composition and orchestration, service invocation is typically scheduled according to execution plans, whose topology establishes whether different services are to be invoked in parallel or in a sequence. In the latter case, we may have a configuration, called pipe join, in which the output of a service is used as input for another service. When the services involved in a pipe join output results sorted by score, the problem arises of efficiently determining the join tuples (aka combinations) with the highest combined scores. In this paper we study different execution strategies related to the pipe join configuration. First, we consider a strategy that minimizes the access costs to achieve a target number of combinations. Then, we propose a strategy that explicitly considers the scores of the output tuples in order to provide deterministic guarantees that the top-k combinations have been found. Finally, a hybrid strategy is presented.
Prediction of Gene Ontology Annotations Based on Gene Functional Clustering

We propose an algorithm that predicts potentially missing Gene Ontology annotations, in order to speed up the time-consuming annotation curation process. The proposed method extends a previous work based on the singular value decomposition of the gene-term annotation matrix and incorporates gene clustering, based on gene functional similarity computed by means of the Gene Ontology annotations. We tested the prediction method by performing K-fold cross-validation on the genomes of two organisms, Saccharomyces cerevisiae (SGD) and Drosophila melanogaster (FlyBase).
Compression noise estimation and reduction via patch clustering

Images compressed at low bit rates usually suffer from annoying artifacts due to coarse quantization of transform coefficients. In this paper, we propose a soft-thresholding scheme to reduce compression noise with content-based noise level estimation. In the proposed method, a compressed image is divided into multiple similar image patch groups, and the compression noise is estimated from every group respectively based on coefficient distribution in transform domain. For each group of similar patches, soft-thresholding is applied to the singular values in the singular value decomposition (SVD) of every group of similar patches. The threshold is adaptively determined based on the standard deviation of image signals and compression noise. Finally, quantization constraint is applied to estimated images to avoid over-smoothing. Extensive experimental results show that the proposed method improves the quality of compressed images obviously, and outperforms state-of-the-art denoising algorithms significantly.
CNN-Based Bi-Directional Motion Compensation for High Efficiency Video Coding

The state-of-the-art High Efficiency Video Coding (HEVC) standard adopts the bi-prediction to improve the coding efficiency for B frame. However, the underlying assumption of this technique is that the motion field is characterized by the block-wise translational motion model, which may not be efficient in the challenging scenarios such as rotation and deformation. Inspired by the excellent signal level prediction capability of deep learning, we propose a bi-directional motion compensation algorithm with convolutional neural network, which is further incorporated into the video coding pipeline to improve the performance of video compression. Our network consists of six convolutional layers and a skip connection, which integrates the prediction error detection and non-linear signal prediction into an end-to-end framework. Experimental results show that by incorporating the proposed scheme into HEVC, up to 10.5% BD-rate savings and 3.1% BD-rate savings on average for random access (RA) configuration have been observed.
Overview of AVS video standard

The paper overviews the AVS video standard (developed by the Audio Video Coding Standard Working Group of China) in terms of basic features, adopted major techniques and its performance. The current version of the AVS video standard mainly aims at the increasing demand for high definition and high quality video services. It provides a good trade-off solution between complexity and coding efficiency for digital broadcast and digital storage media. Furthermore, having a syntax structure similar to that of the MPEG-2 video standard, it can be easily applied into existing MPEG-2 systems, while significantly improving coding efficiency.
Zero-synthesis view difference aware view synthesis optimization for HEVC based 3D video compression

In this paper, we explore a new scheme for view synthesis optimization (VSO) in the upcoming high efficiency video coding (HEVC) based 3D video compression standard. With the dynamic range analysis of depth value, we present a novel zero-synthesis view difference (ZSVD) model. This model accounts for the compound impact of depth-disparity mapping, texture masking and occlusion information. We further incorporate the ZSVD profile into rate distortion optimization process via the ZSVD adaptive VSO, which is developed by pruning the conventional view rendering algorithm. Experimental results exhibit that the proposed scheme can remarkably reduce the computational complexity with negligible performance loss.
A local-adapted disparity vector derivation scheme for 3D-AVS

In the 3D extension of Audio Video Coding Standard (AVS), i.e. 3D-AVS, the Global Disparity Vector (GDV) derivation technique has been proposed to provide an estimation for Disparity Vector (DV) in inter-view prediction, where the GDV is generated by averaging all Disparity Vectors (DVs) in the latest previously coded frame. The prediction accuracy of GDV may be however limited by the lack of local adaptivity. In this paper, we introduce a novel Local Disparity Vector (LDV) derivation scheme. Specifically, the DV of the current block is calculated from the DVs within a neighboring region, whose size can be adaptively expanded to increase the robustness and accuracy. Experimental results show that the proposed LDV derivation method can provide around 2.12% and 1.37% bitrate reductions for compressed views and synthesized views compared with the GDV scheme, respectively.
Local-constrained quadtree plus binary tree block partition structure for enhanced video coding

In the latest Joint Video Exploration Team (JVET) development, a quadtree plus binary tree (QTBT) block partition structure is proposed for enhanced video coding. Compared to the quadtree block partition in HEVC, QTBT can achieve much better compression performance at the expense of largely increased encoding computational complexity. In this paper, a fast QTBT block partition algorithm based on the local constraint is proposed. Given the decoded information of the previous frame, the partition parameters can be dynamically derived for each coding tree unit (CTU) without any overhead. Furthermore, a constrained binary tree partition algorithm is proposed to reduce the redundancy between the quadtree partition and the binary tree partition. Experimental results have shown that the proposed scheme can speed up QTBT block partition structure by 30% on average without obvious performance decrease, which enables its practical implementations in real application scenarios.
An adaptative bitrate algorithm for DASH

Dynamic adaptive streaming over HTTP (DASH) has been widely used on the Internet. However, DASH does not impose any algorithm to choose video quality. In this paper, an adaptive bitrate switch algorithm for DASH player is proposed. Firstly, the proposed algorithm takes video playback quality, video rate switching frequency and buffer status into account in order to meet the available bandwidth. Secondly, several measures are designed specially to improve user visual quality. Besides, the proposed player takes a sub-optimal algorithm to avoid video playback interruptions. Experimental results demonstrate that the proposed player can provide better performance compared with Bitdash player in several aspects, such as video quality switch frequency, bandwidth utilization and subjective visual experience.
Towards Accurate and Efficient Image Quality Assessment with Interest Points

In recent years, the dramatic development of cloud computing, referring to as the applications and services implemented over Internet, has been witnessed and draws many attentions from both academia and industry. Objective image quality assessment (IQA) is fundamental to a broad range of applications throughout the fields of image processing and computer vision. There is a huge desire in exploiting new design of IQA model which is not only accurate but also efficient for fitting the requirement under the background of big data. Many successful models have been built for accurate prediction of the perceptual visual quality, where some typical characteristics of human visual system (HVS) are utilized and incorporated in IQA systems. The well-known foveation effect assumes that the regions around the fixation points are much more attractive to human eyes, thus the quality of these regions would significantly influence the overall visual quality. In this paper, we analyze the correlations between the fixation point and quality assessment by integrating several state-of-the-art interest point detection algorithms into IQAs. Experimental results on public database demonstrate that the addictive information of interest point is helpful for improving accuracy of popular IQA models, and meanwhile dramatically reducing the computational complexity. Furthermore, the parameter impacts on IQA performance are thoroughly analyzed showing that the parameters should be carefully designed for different IQA models as well as viewing conditions.
Deep convolutional network based image quality enhancement for low bit rate image compression

In this contribution, a novel image quality enhancement algorithm based on convolutional network is proposed for low bit rate image compression. Specifically, a downsample procedure is performed to generate lower resolution image for low bit rate compression. While the decoder side, upsample is to be performed firstly to the original resolution. Image quality is further enhanced by the proposed convolutional deep network. In particular, an optional image quality improvement network can be utilized for further enhancement after the first network. With the help of deep network, more detailed and high-frequency information can be recovered while maintaining the consistency of contour area, leading to better visual quality. Another benefit of this approach lies in that the proposed approach is fully compatible with all third-party image codec pipeline. Experimental result shows that the proposed scheme significantly outperforms JPEG in low bit rate image compression.
The second generation IEEE 1857 video coding standard

A new generation video coding standard developed by IEEE 1857 working group will be published as IEEE 1857.4, which assumes the work on the first generation of IEEE 1857 video coding standard IEEE std. 1857-2013(IEEE 1857.1) and targets to double the coding efficiency of IEEE std. 1857-2013. This paper provides an overview of the forthcoming IEEE 1857.4 video coding standard, including the background and the key coding tools used in IEEE 1857.4. The performance comparisons between IEEE 1857.4 and the state-of-the art coding standards are also provided.
Parallelizing video transcoding with load balancing on cloud computing

Cloud computing is emerging as a very promising technology for computing and storage services. However, the multi-resources load balancing over heterogeneous cluster or cloud is a NP-hard problem. To obtain an optimized solution, in this paper, we propose a heuristic algorithm named Minimum Longest Queue Finish Time (MLFT). In the proposed scheme, we first divide the high computation task into multiple sub-tasks, and re-organize all the tasks into multiple task queues to shorten the entire finish time of all the tasks submitted to the cluster and launched in parallel according to load balancing. In the task division process, an adaptive segmentation algorithm is proposed according to the complexity and maximum segmentation granularity of the input task. Based on the proposed algorithm, an efficient parallel video transcoding framework with cloud computing is presented. Experimental results show that the proposed algorithm outperforms the existing algorithms significantly on the entire finish time of the tasks and approaches to the optimal solution closely.
AVS2-video coding standard - An application-oriented and high perforamce video coding standard

After years of hard work, the first generation of Audio-video coding standard (AVS1) has become an international standard and been widely used in digital satellite HDTV broadcasting. However, the growing popularity of high resolution videos and the emergence of 3D, scalable, 10-bit and stationary scene videos raise a higher requirement for video coding standards. The second generation of Audio-video coding standard (AVS2) is an application-oriented video coding standard aiming at higher video coding efficiency. In AVS2, different profiles are set for different applications, such as main profile for general application, especially for high resolution videos, and scene profile for stationary scene videos. Coding tools are designed by jointly considering the coding complexity and performance gain. This paper will give an overview of AVS2. Results show that main profile has as high as 49.5% and 44.0% bit rate saving in non-low-delay and low-delay configurations compared to AVS1. And scene profile has as much as 52.7% and 28.5 % coding gain in non-low-delay and low-delay configuration compared to main profile.
Sample edge offset compensation for HEVC based 3D Video Coding

In this paper, an in-loop sample edge offset compensation (SEOC) framework is proposed for High Efficiency Video Coding (HEVC) based 3D video (3D-HEVC) coding. The framework targets at improving the reconstruction quality of the depth images, especially in edge areas. In a typical 3DV system, depth images are used for synthesizing the virtual views, therefore preserving high quality depth images, especially the edge information is important. However, the ringing artifacts may be introduced at the depth edge areas due to the compression distortion of the depth images. The SEOC framework resolves this problem, after reconstruction, by identifying each edge pixel and enhancing it of the full depth image with values coded in the bitstream. Experimental results demonstrate that, compared with the original 3D-HEVC, the proposed algorithm can achieve about 6% bitrate saving on average.
Video Coding With Rate-Distortion Optimized Transform

Block-based discrete cosine transform (DCT) has been successfully adopted into several international image/video coding standards, e.g., MPEG-2, H.264/AVC, as it can achieve a good tradeoff between performance and complexity. Although DCT theoretically approximates the optimum Karhunen-Loève transform under first-order Markov conditions, one fixed set of transform basis functions (TBF) cannot handle all the cases efficiently due to the non-stationary nature of video contents. To further improve the performance of block-based transform coding, in this paper, we present the design of rate-distortion optimized transform (RDOT) which contributes to both intraframe and interframe coding. The most important property which makes a difference between RDOT and the conventional DCT is that, in the proposed method, transform is implemented with multiple TBF candidates which are obtained from off-line training. With this feature, for coding each residual block, the encoder is capable to select the optimal set of TBF in terms of rate-distortion performance, and better energy compaction is achieved in the transform domain. To obtain an optimum group of candidate TBF, we have developed a two-step iterative optimization technique for the off-line training, with which the TBF candidates are refined at each iteration until the training process becomes converged. Moreover, analysis on the optimal group of candidate TBF is also presented in this paper, with a detailed description of a practical implementation for the proposed algorithm on the latest VCEG key technical area software platform. Extensive experimental results show that, compared with the conventional DCT-based transform scheme adopted into the state-of-the-art H.264/AVC video coding standard, significant improvement of coding performance has been achieved for both intraframe and interframe coding with our proposed method.
Visual Information Evaluation With Entropy of Primitive

In this paper, we overview the recent work on entropy of primitive (EoP), including its concept, design, extension, and mathematical analysis in evaluating the visual information of natural images. The design philosophy of EoP is establishing an entropy model that quantifies the visual information based on patch-level sparse representation, due to the close relationship between sparse representation and the hierarchical cognitive process of human perception. Furthermore, based on the concept and definition of EoP, we also demonstrate several applications, including just noticeable difference estimation and visual quality assessment. The future research directions of visual information evaluation are also envisioned, where we can perceive both promises and challenges.
Parallel AMVP candidate list construction for HEVC

Advanced motion vector prediction (AMVP) is one of the most important inter prediction coding tools adopted in the state-of-the-art HEVC coding standard, which does great effect on the coding efficiency. However, the current AMVP design is highly sequential and thus restricts the throughput both on the encoder and the decoder sides. To facilitate the parallel processing and enlarge the throughput, a parallel AMVP candidate list (AMVPCL) construction solution is proposed. The proposed parallel scheme consists of a three level fine granularity solutions. The first level is a CU-based approach and it constructs AMVPCL of all PUs in the same CU in parallel. The second level is also at CU level but it generates a single set of AMVPCL for all PUs inside a CU. Specifically, we only apply this method to 8×8 CU to balance the parallelism degree and rate-distortion performance. The third level is a CU-group based approach, in which AMVPCL of all PUs in the same CU-group are constructed in parallel. Experimental results show the proposed algorithm can efficiently achieve parallel motion estimation with negligible 0.0%~1.3% BD-rate loss at different degree of parallelism.
A flexible reference picture management scheme

Reference picture management is an important part in video encoding and decoding process, and has great influence on the coding performance. In this paper, a flexible reference picture management scheme is proposed for the second generation of Audio Video Coding Standard (AVS2). In the proposed scheme, reference configuration set (RCS), which consists of reference picture information, is used to manage the reference picture. Based on RCS, the reference picture set for current coding picture can be arbitrarily configured. Experimental results show that the proposed flexible reference picture management scheme achieves significant bitrate reduction in AVS2 encoder. For low delay P and random access common test condition, the average coding gain can be up to 4.3% and 5.1% respectively.
Wavelet inpainting driven image compression via collaborative sparsity at low bit rates

To overcome the unsatisfactory encoding quality of conventional image compression methods at low bit rates, the idea of downsampling prior to encoding and upsampling after decoding turns out to be a good solution. Based on this paradigm, we propose a low-bit-rate image compression algorithm by use of the novel wavelet inpainting technique via collaborative sparsity. Superior to the existing methods which operate the sampling in the space domain, we merge the wavelet transform in the downsampling stage, which is verified to be able to preserve much more information. By investigating the local two-dimensional sparsity and the nonlocal three-dimensional sparsity of the image simultaneously, a collaborative sparsity model is exploited to restore the full-resolution image from the decoded downsampled image. Finally a Split Bregman based iterative algorithm is developed to solve the optimization problem. Experimental results demonstrate obvious visual quality improvements, as well as PSNR gains, compared to the state-of-the-art methods under various low bit rates.