nils-herrmann/clean_scirepeval huggingface dataset:
Multiple Description Coding of 3D Meshes Based on Forward Error Correction

This work presents a multiple description coding (MDC) scheme for compressed three dimensional (3D) meshes based on forward error correction (FEC). It allows flexible allocation of coding redundancy for reliable transmission over error-prone channels. The proposed scheme is based on progressive geometry compression, which is performed by using wavelet transform and modified SPIHT algorithm. The proposed algorithm is optimized for varying packet loss rates (PLR) and channel bandwidth. Modeling distortion-rate function considerably decreases computational complexity of bit allocation.
A spatiotemporal no-reference video quality assessment model

Many researchers have been developing objective video quality assessment methods due to increasing demand for perceived video quality measurement results by end users to speed-up advancements of multimedia services. However, most of these methods are either Full-Reference (FR) metrics, which require the original video or Reduced-Reference (RR) metrics, which need some features extracted from the original video. No-Reference (NR) metrics, on the other hand, do not require any information about the original video; hence, are much more suitable for applications like video streaming. This paper presents a novel, objective, NR video quality assessment algorithm. The proposed algorithm is based on utilization of spatial extent of video, temporal extent of video using motion vectors, bit rate, and packet loss ratio. Test results obtained using LIVE video quality database demonstrate the accuracy and robustness of the proposed metric.
Driver aggressiveness detection using visual information from forward camera

Among the human related factors, aggressive driving behavior is one of the major causes of traffic accidents . On the other hand, detection and characterization of driver aggressiveness is a challenging task since there exist different psychological causes behind it. However, information about the driver behavior could be extracted from the data that is collected via different sensing devices. This paper presents a method to detect driver aggressiveness using only visual information provided by forward camera. The proposed method is based on detection of the road lines and the vehicles on the road and extracts information related with road lane departure rate, speed of the vehicle and possible forward collision time. Using these extracted features, a classifier is utilized in order to detect if driver shows an aggressive driving behavior. The proposed method is tested by a subjective testing method using 76 different driving sessions and achieved 90.4% success.
End-to-End Stereoscopic Video Streaming System

Today, stereoscopic and multi-view video are among the popular research areas in the multimedia world. In this study, we have designed and built a platform consisting of stereo-view capturing, real-time transmission and display. At the display stage, end users view video in 3D by using polarized glasses. Multi-view video is compressed in an efficient way by using multi-view video coding techniques and streamed using standard real-time transport protocols. The entire system is built by modifying available open source systems whenever possible. Receiver can view the content of the video built from multiple channels as mono or stereo depending on its display and bandwidth capabilities
Error Resilient Layered Stereoscopic Video Streaming

In this paper, error resilient stereoscopic video streaming problem is addressed. Two different forward error correction (FEC) codes namely systematic LT and RS codes are utilized to protect the stereoscopic video data against transmission errors. Initially, the stereoscopic video is categorized in 3 layers with different priorities. Then, a packetization scheme is used to increase the efficiency of error protection. A comparative analysis of RS and LT codes are provided via simulations to observe the optimum packetization and UEP strategies.
Relaxed Spatio-Temporal Deep Feature Aggregation for Real-Fake Expression Prediction

Frame-level visual features are generally aggregated in time with the techniques such as LSTM, Fisher Vectors, NetVLAD etc. to produce a robust video-level representation. We here introduce a learnable aggregation technique whose primary objective is to retain short-time temporal structure between frame-level features and their spatial interdependencies in the representation. Also, it can be easily adapted to the cases where there have very scarce training samples. We evaluate the method on a real-fake expression prediction dataset to demonstrate its superiority. Our method obtains 65% score on the test dataset in the official MAP evaluation and there is only one misclassified decision with the best reported result in the Chalearn Challenge (i.e. 66.7%). Lastly, we believe that this method can be extended to different problems such as action/event recognition in future.
Real time streaming of 3D video from embedded platforms to mobile devices

This study presents the stereo video capture and compression on embedded platforms in real time and streaming of the video to mobile devices over a wireless network, as an end-to-end system. The major components of the system are the state-of-the art compression standard H.264/AVC used with side-by-side representation of stereo video on the OMAP3530 SoC (SystemonChip) ARM processor and the mobile device with autostereoscopic display at the receiver side. With the presented architecture, 3D video communication between the mobile users connected to a wireless network is made possible. Furthermore, performance tests are conducted for the system and results are provided in this study.
Distributed 3D dynamic mesh coding

In this paper, we propose a distributed 3D dynamic mesh coding system. The system is based on Slepian and Wolf's and Wyner and Ziv's information-theoretic results. Our system extends the ideas in distributed video coding to 3D dynamic meshes with constant connectivity. The connectivity of the sequence and key frames are encoded and decoded by a conventional static mesh coder. The Wyner-Ziv frames are encoded independent of key frames but decoded jointly with decoded key frames. The joint decoding is performed by the low density parity check codes and the side information generated by linear interpolation of decoded key frames. Experimental results show that better rate-distortion performance is obtained compared to encoding each frame by a static mesh coder.
Spatiotemporal No-Reference Video Quality Assessment model on distortions based on encoding

With increasing demand on video applications, the video quality estimation became an important issue of today's technological world. There are different researchers and institutions working on video quality estimation. Most of the objective Video Quality Assessment (VQA) algorithms are Full-Reference (FR) metrics, and they require the original video. Metrics which require some features extracted from reference video are called as Reduced-Reference (RR). Additionally, No-Reference (NR) metrics do not require any information about the original video. Therefore, NR metrics are much suitable for online applications such as video streaming. A novel, objective, NR video quality assessment metric is proposed in this study. The proposed algorithm is based on utilization of spatial extent of video, temporal extent of video using motion vectors and bit rate. Test results obtained using the bit streams which have distortions based on encoding from LIVE video quality database. Results indicate the proposed metric is an accurate and robust algorithm.
Super-resolution using multiple quantized images

In this paper, we study the effect of limited amplitude resolution (pixel depth) in super-resolution problem. The problem we address differs from the standard super-resolution problem in that amplitude resolution is considered as important as spatial resolution. We study the trade-off between the pixel depth and spatial resolution of low resolution (LR) images in order to obtain the best visual quality in the reconstructed high resolution (HR) image. The proposed framework reveals great flexibility in terms of pixel depth and number of LR images in super-resolution problem, and demonstrates that it is possible to obtain target visual qualities with different measurement scenarios including images with different amplitude and spatial resolutions.
Packet Loss Resilient Transmission of 3D Models

This paper presents an efficient joint source-channel coding scheme based on forward error correction (FEC) for three dimensional (3D) models. The system employs a wavelet based zero-tree 3D mesh coder based on progressive geometry compression (PGC). Reed-Solomon (RS) codes are applied to the embedded output bitstream to add resiliency to packet losses. Two-state Markovian channel model is employed to model packet losses. The proposed method applies approximately optimal and unequal FEC across packets. Therefore the scheme is scalable to varying network bandwidth and packet loss rates (PLR). In addition, distortion-rate (D-R) curve is modeled to decrease the computational complexity. Experimental results show that the proposed method achieves considerably better expected quality compared to previous packet-loss resilient schemes.
Importance of side information generation for distributed video coding

Distributed Video Coding (DVC) is a new coding paradigm, based on two information-theoretic results, which allows exploiting source statistics at the decoder only. This architecture, therefore, enables very simple encoders to be used in video coding. Wyner-Ziv video coding is a particular case of DVC which deals with lossy source coding where side information is available at the decoder only. In this paper, the performance improvement of Wyner-Ziv coding by using better side information generated by motion compensated frame interpolation algorithms proposed in literature is examined.
Represent, reduce, classify: The essential stages for scene recognition

In this paper, scene recognition problem is investigated in detail by exploiting scene representation, dimension reduction and classification stages. Unlike the other studies, the proposed algorithm has preferred to model the overall structure of the scene instead of an object-based proposal. For that purpose, some of the visual representations like MPEG-7, Gist, BoW, Vlad and Fisher, are classified singly or jointly with Support Vector Machine and Random Forest. The evaluation tests are conducted on MIT indoor dataset [2] and from the results, %31 average precision has been attained by combining Scalable Color, Homogeneous Texture and Vlad with Support Vector Machine.
An abstraction based reduced reference depth perception metric for 3D video

In order to speed up the wide-spread proliferation of the 3D video technologies (e.g., coding, transmission, display, etc), the effect of these technologies on 3D perception should be efficiently and reliably investigated. Using Full-Reference (FR) objective metrics for this investigation is not practical especially for “on the fly” 3D perception evaluation. Thus, a Reduced Reference (RR) metric is proposed to predict the depth perception of 3D video in this paper. The color-plus-depth 3D video representation is exploited for the proposed metric. Since the significant depth levels of the depth map sequences have great influence on the depth perception of users, they are considered as side information in the proposed RR metric. To determine the significant depth levels, the depth map sequences are abstracted using bilateral filter. Video Quality Metric (VQM) is utilized to predict the depth perception ensured by the significant depth levels due to its well correlation with the Human Visual System (HVS). The performance assessment results present that the proposed RR metric can be utilized in place of a FR metric to reliably measure the depth perception of 3D video with a low overhead.
Improved prediction for layered predictive animated mesh compression

In this paper, we deal with layered predictive compression of animated meshes represented by series of 3D static meshes with same connectivity. We propose two schemes to improve the prediction. First improvement is using weighted spatial prediction rather than averaging neighbor vertices. The second improvement is a novel predictor based on rotation angle of incident triangles in current and previous frames. The experimental results show that around 6–10 % bitrate reduction can be achieved by replacing the spatial prediction in the reference coder with the proposed weighted spatial prediction and 9–18 % bitrate reduction is possible with the proposed angle based predictor using weighted spatial prediction, depending on the content and quantization level.
Stereoscopic video streaming with end-to-end modeling

In this work, we propose a stereoscopic video streaming system that uses rateless codes (Raptor codes) as the error protection scheme. Initially, we model the rate-distortion (RD) curve of video encoder and performance of channel codec. Then, we estimated the distortion on the stereoscopic video quality caused by packet losses. Finally, analytical models and estimated single packet loss distortions are used to minimize the end-to-end distortion and obtain optimal encoder bit rates and unequal error protection (UEP) rates. The simulation results clearly demonstrates the significant quality gain against the non-optimized schemes.
Hyperspectral imagery superresolution

Despite their high spectral resolution, hyperspectral images have low spatial resolution which adversely affects the applications that use hyperspectral images. In this study, instead of the traditional way of using spectral images, abundances of the endmembers are used in resolution enhancement. In the proposed method, first, endmembers are extracted with the SISAL algorithm. Then, the abundance maps are estimated using FCLS. From the low resolution abundance maps, high resolution abundance maps are obtained with a total variation based minimization. Finally, high resolution hyperspectral images are constructed from high resolution abundance maps. The proposed method is tested on real hyperspectral images. The experimental results and comparative analysis show the effectiveness of the proposed method.
Texture and edge preserving multiframe super-resolution

Super-resolution (SR) image reconstruction refers to methods where a higher resolution image is reconstructed using a set of overlapping aliased low-resolution observations of the same scene. Although edge preservation has been a widely explored topic in SR literature, texture-specific regularisation has recently gained interest. In this study, texture-specific regularisation is handled as a post-processing step. A two stage method is proposed, comprising multiple SR reconstructions with different regularisation parameters followed by a restoration step for preserving edges and textures. In the first stage, two maximum-a-posteriori estimators with two different amounts of regularisation are employed. In the second stage, pixel-to-pixel difference between these two estimates is post-processed to restore edges and textures. Frequency selective characteristics of discrete cosine transform and Gabor filters are utilised in the post-processing step. Experiments on synthetically generated images and real experiments demonstrate that the proposed methods give better results compared with the state-of-the-art SR methods especially on textures and edges.
Scene nudity level detection with deep nets

In this paper, we present an approach that can detect scene nudity level with high precision using different deep net configurations. For this purpose, a recent approach  which has intense and very deep convolution layers is used. During net modelling, we strive to obtain most successful net configuration by comparing different Dropout models and image sizes -64 × 64, 128 × 128-. Additionally, leveraging the generalization capability of Support Vector Machine (SVM), improvement on success rate is demonstrated by retraining the features obtained at different output levels of the nets with SVM. At test and training stages, scene is investigated under three nudity levels: regular, semi-nudity and full-nudity. In order to evaluate false alarm rates of the net models, tests are conducted on different datasets which are SUN2012, LEAR Human and a dataset contains only semi-nudity samples besides validation set determined for each class. The results indicate that high precision rates can be achieved with low false alarm rate exploiting deep net models.
Breast segmentation in infrared images

Breast cancer detection with infrared imaging is a technique that is hazardous radiation free, increases the early detection chance and has a high detection probability. Asymmetry analysis is used for the diagnosis and the analysis is done using the breast segments obtained from the infrared image. In this work, a breast segmentation system based on parabolic Hough transform is presented. Besides, a filtering method using connected components is proposed to increase both the computation speed and the segmentation performance.
Evaluation of image fusion and contrast enhancement algorithms for fusing LWIR and MWIR images

Image fusion is an important application for collecting complementary and redundant information observed by different sensors into a single image. In this paper, various image fusion algorithms are compared for long-wave infrared (LWIR) and mid-wave infrared (MWIR) images. Infrared images were enhanced by various contrast enhancement algorithms and the effect of the enhancement on the fusion results is evaluated.
Video content analysis method for audiovisual quality assessment

In this study a novel, spatio-temporal characteristics based video content analysis method is presented. The proposed method has been evaluated on different video quality assessment databases, which include videos with different characteristics and distortion types. Test results obtained on different databases demonstrate the robustness and accuracy of the proposed content analysis method. Moreover, this analysis method is employed in order to examine the performance improvement in audiovisual quality assessment when the video content is taken into consideration.
Motion analysis using 3D Gabor kernels

Today human motion understanding has become one of the most active research areas in computer vision. This is due to promising applications in areas such as visual surveillance, human performance analysis, computer-human interfaces (robotic interaction with humans), content-based video retrieval/storage and virtual reality. This work is focused on recognizing human actions. A novel motion analysis method is proposed. Gabor kernels, which are appears to be the most successful approach for 2D texture segmentation and modeling, are extended to the time domain to extract temporal texture features as well. Human motion is represented by states and a dynamic time warping based approach is used for motion matching.
Motion and Disparity Aided Stereoscopic Full Frame Loss Concealment Method

Stereoscopic video is one of the emerging research areas especially among the video coding community. Along with the studies for efficiently compressing the stereoscopic and multiview video, new error concealment and error protection methods are also necessary to overcome the problems due to erroneous channel conditions in practical applications. In this paper we propose a full frame loss concealment algorithm for stereoscopic sequences. The proposed method uses redundancy and disparity between the two views and motion information between the previously decoded frames to estimate the lost frame. The results show that, the proposed algorithm outperforms the monoscopic methods when they are applied to the same view as they are simulcast coded.
Evaluation of disparity map characteristics for stereo image coding

In order to compress stereo image pairs effectively, disparity compensation is the most widely used method. In this paper we examined the effects of using different disparity maps and their properties in an embedded JPEG2000 based disparity compensated stereo image coder. These properties include the block size, estimation method and the resulting entropy of the disparity map. Experimental results show that basic block matching gives better results than ground truth, especially on occluded regions and boundaries.
Unequal Error Protection for Stereoscopic Video Streaming

The utilization of forward error correction (FEC) schemes for stereo video streaming is investigated. Stereo video is categorized in 3 layers and each layer is protected with different protection ratios for efficient streaming. Systematic Reed-Solomon (RS) and Luby Transform (LT) codes are utilized as the error protection schemes. Detailed simulations are performed in order to observe the optimum unequal error protection (UEP) strategies for the defined video layers. Moreover, as a result of these simulations the performance comparison of RS and LT codes for video streaming is provided.
Error resilient video coding using FMO (Flexible Macroblock Ordering) in wired and wireless communication

Nowadays, wire and wireless communication is getting easier because of the progress in video codec technology. However, since encoders increase the dependency among frames, a small error during the transmission can effect the video quality substantially. Error resilient coding is one of the methods to avoid or reduce the the amount of corruption in video by altering the encoding algorithm. There are several coding tools to reach this aim. One of them is FMO(Flexible Macroblock Ordering) which provides us with ordering macroblocks of the frames flexibly. Therefore macroblocks can be grouped according to a special pattern. There are seven FMO types. Six of them have definite ordering pattern and the last one, called explicit type, can get any order. In this paper two explicit type algorithms, one of which is new, are explained and the performance of different FMO types in wired and wireless communication are evaluated.
A standards-based, flexible, end-to-end multi-view video streaming architecture

In this paper we propose a novel framework for the streaming of 3-D representations in the form of Multi- View Videos (MVV). The proposed streaming system is completely standards based, flexible and backwards compatible in order to support monoscopic streaming to legacy clients. We demonstrate compatibility of the proposed system with various possible encoding schemes and operating scenarios. In the current implementation, the MVVs in the server are compressed using a simplified form of MVC with negligible loss of compression efficiency and streamed using Real Time Streaming Protocol (RTSP), Session Description Protocol (SDP) and Real Time Protocol (RTP) to the clients. We describe our extensions to SDP and discuss a preliminary RTP payload format for MVC. The clients in this implementation perform basic error concealment to reduce the effects of packet losses and decode MVC in near-real-time. The modular clients can display decoded 3-D content on a multitude of 3-D display systems.