nils-herrmann/clean_scirepeval huggingface dataset:
A hybrid approach for efficient color gamut mapping

Ultra High Definition (UHD) and High Definition (HD) Television standard recommendations support different color gamuts with the HD gamut much smaller than that of the UHD one. To adapt UHD content to the restricted gamut of HD televisions, a process known as gamut mapping is required. This gamut mapping projects out-of-gamut colors inside the targeted color gamut. Gamut mapping can be performed in any color space and using different projection methods. In this paper, we present a hybrid gamut mapping approach which selects one combination of color space and projection method for each UHD color representable. The selection is based on the CIE ΔE2000 metric. Results show improvements in terms of CIE ΔE2000 when comparing original and projected colors over existing methods.
A color gamut mapping scheme for backward compatible UHD video distribution

The new Ultra High Definition (UHD) standard digital imagery can represent much more color information than High Definition (HD) and Standard Definition (SD). Currently most manufactured displays support UHD colors while UHD is being deployed for content production. However not all service providers have updated their pipeline thoroughly. Thus, the enduser that buys a UHD display would not be able to benefit from the wider UHD color range. In this paper, we propose an invertible gamut mapping from UHD colors to HD colors so that UHD displays can reconstruct UHD colors, while HD displays are addressed directly using legacy video delivery pipeline. The proposed color mapping scheme allows the mapped signal to be converted back to the original signal with minimal perceptual error so that the viewers' quality of experience (QoE) is preserved. Our method includes a parameter that adjusts the trade-off between the quality of the HD content and that of the UHD content. Our experiment results provide a guideline on how to strike a balance between color errors in the mapped signal and the retrieved one.
Compression efficiency of HDR/LDR content

High Dynamic Range (HDR) imaging is capable of delivering a wider range of luminance and color gamut compared to Standard Dynamic Range (SDR), offering to viewers a visual quality of experience close to that of real-life. In this study, we evaluate the quality of coded original HDR streams and HDR streams reconstructed from SDR videos and metadata, both compressed by the HEVC standard. Our evaluations have shown that the single HDR approach is largely preferred over the SDR counterpart.
Hybrid OFDMA/CSMA Based Medium Access Control for Next-Generation Wireless LANs

Existing medium access control (MAC) schemes for wireless local area networks (WLAN) have been shown to lack scalability in crowded networks, and efficiency in supporting heterogeneous traffic types. These issues are mostly due to the use of random multiple access techniques in the MAC layer. The design of these techniques is highly linked to the choice of the underlying physical (PHY) layer technology. The advent of new PHY schemes that are based on orthogonal frequency division multiple access (OFDMA) provides new opportunities for devising more efficient MAC protocols. We propose a new adaptive MAC design based on OFDMA technology. The design uses OFDMA to reduce collision during transmission request phases, and makes channel access more predictable. To improve efficiency, we combine the OFDMA access with a carrier sense multiple access (CSMA) scheme. Data transmission opportunities are assigned through an access point that can schedule traffic streams in both time and frequency (subchannels) domains. We demonstrate the effectiveness of the proposed MAC and compare it to existing mechanisms through simulation experiments and by deriving an analytical model for the operation of the MAC in saturation mode.
Video-based Human Fall Detection in Smart Homes Using Deep Learning

Automatic human fall detection is a challenging task of healthcare in smart homes, and video cameras have been proved to be efficient in addressing this problem. Although existing methods perform relatively well, they are all built upon "hand-crafted" features, thus constraining the performance of the model to some presumed conditions and scenarios, and making it vulnerable to any deviation from the assumed settings. In this paper, we propose a deep-learning-based approach for human fall detection, using long short-term memory neural network. Our model is not restricted to any specific circumstances, and performance evaluations show that it outperforms all the existing methods.
3D video quality metric for mobile applications

In this paper, we propose a new full-reference quality metric for mobile 3D content. Our method is modeled around the Human Visual System, fusing the information of both left and right channels, considering color components, the cyclopean views of the two videos and disparity. Our method is assessing the quality of 3D videos displayed on a mobile 3DTV, taking into account the effect of resolution, distance from the viewers' eyes, and dimensions of the mobile display. Performance evaluations showed that our mobile 3D quality metric monitors the degradation of quality caused by several representative types of distortion with 82% correlation with results of subjective tests, an accuracy much better than that of the state-of-the-art mobile 3D quality metric.
Bitrate reduction in asymmetric stereoscopic video with low-pass filtered slices

In 3D video compression, similarities between the two views can be helpful in efficient video compression. One way to take advantage of these similarities is to have one of the views at high quality and lower the quality of the other view. In this study, we measured the bitrate reduction resulted from low-pass filtering horizontal slices in both views, with the corresponding slice in the other view kept at the original quality. Results showed that we can reduce the required bitrate for the stereoscopic video while keeping the 3D video quality close to the original video.
A fast mode decision algorithm for downscaled transcoding of H.264 preencoded video

In this paper, we present a fast mode decision method for transcoding pre-encoded H.264 video to a lower resolution. The algorithm reduces the computational load of the mode decision process during transcoding by adaptively considering information from the residual of the pre-encoded video and some intermediate results obtained in the re-encoding process. A performance improvement of 37% has been achieved compared to the best existing downscaling transcoding process known as the area-weighted vector median filter (AWVM).
Fast block size prediction for MPEG-2 to H.264/AVC transcoding

One objective in MPEG-2 to H.264 transcoding is to improve the H.264 compression ratio by using more accurate H.264 motion vectors. Motion re-estimation is by far the most time consuming process in video transcoding, and improving the searching speed is a challenging problem. We introduce a new transcoding scheme that uses the MPEG-2 DCT coefficients to predict the block size partitioning for H.264. Performance evaluations have shown that, for the same rate-distortion performance, our proposed scheme achieves an impressive reduction in the computational complexity of more than 82% compared to the full range motion estimation used by H.264.
Health Drive: Mobile Healthcare Onboard Vehicles to Promote Safe Driving

This paper proposes Health Drive, a novel mobile healthcare platform with context-aware multiple-sensor integration to promote safe driving. Health Drive employs a multi-tier vehicular social network (M-VSN) architecture that consists of three tiers: network tier, mobile device tier and cloud tier. The network tier provides communication supports, and the mobile device tier works in parallel with the cloud tier to integrate and interpret diverse sensing data. The three tiers work together to provide a seamless solution that can efficiently collect and interpret diverse sensing data in the heterogeneous environments found in vehicular networks, to deliver personalized service to drivers for safety improvement in real-time. We evaluate the system performance of Health Drive and provide results of practical experiments to show the desired functionality and feasibility of Health Drive in the real-world for safety improvement in transportation.
Video Packetization Techniques for Enhancing H.264 Video Transmission over 3G Networks

Transmission of video over a wireless network is a challenging task due to the error prone characteristics of the wireless link. This application requires the video encoding process to offer error resilience features in order to deal with the high packet loss probability of wireless networks. H.264, the newest and most efficient video compression standard, offers several error resiliency features. H.264 also offers network support by utilizing a network abstraction layer (NAL) and encapsulating individually decodable video slices in NAL units. The size of the video slices, or packets, has a significant effect on the overall performance of a video application in wireless networks. In this paper we study the possibility of determining the optimal packet size of encoded video that minimizes the packet loss rate and maximizes the video quality during transmission over 3G networks. Through simulation experiments, we show that smaller slices are more favorable, but encoding inefficiency and increase in overhead set a limit on the minimum acceptable packet size.
Non-intrusive human activity monitoring in a smart home environment

Non-intrusive activity monitoring of occupants in a home environment plays an important role in developing the next generation of smart environments and remote health monitoring systems. One important challenge in this research area is lack of a comprehensive dataset. In this paper, we introduce a new dataset related to a smart home environment, which can be used for human activity recognition. In addition, we benchmark our proposed human action recognition algorithm and some other state-of-the-art methods using our dataset.
Online-Learning-Based Mode Prediction Method for Quality Scalable Extension of the High Efficiency Video Coding (HEVC) Standard

SHVC, the scalable extension of High Efficiency Video Coding (HEVC), uses advanced inter-layer prediction features in addition to the advanced compression tools of HEVC to improve the compression performance. Using combined features has brought us improved compression performance at the cost of huge computational complexity for the SHVC encoder. This complexity is mainly because of the the inter/intra-prediction mode search of the coding units. The focus of this study is on developing an efficient complexity reduction for quality scalability of SHVC encoder, with the intention to facilitate the adoption of SHVC for real-time applications. In this regard, first, we build a probabilistic model that uses the mode information and motion homogeneity of already encoded blocks in the enhancement layer (EL) and the base layer to predict the probabilities of all the available inter/intra modes of the to-be-coded block in the EL. Then, we propose an online-learning-based fast mode, assigning (FMA) method that uses the proposed probabilistic model to predict the mode of the to-be-coded block in the EL. Performance evaluation shows that our proposed FMA method reduces the total execution time of the SHVC encoder by 45.40% on average compared with unmodified SHVC codec while maintaining the overall video quality.
Efficient Utilization of Error Protection Techniques for Transmission of Data-Partitioned H.264 Video in a Capacity Constrained Network

We propose an efficient error protection technique for data-partitioned H.264 video in a capacity constrained network. Our scheme maximizes video quality by choosing the optimal point in the application layer and Medium Access Control (MAC) layer redundancy. We have shown that, in a capacity constrained network and highly lossy environment, neither Forward Error Correction (FEC) nor retransmissions alone can result in optimum performance. Instead, it is the combination of these two techniques that effectively reduces the overall loss.
Non-negative sparse coding for human action recognition

We consider the problem of human action recognition using non-negative sparse representation of extracted features from spatiotemporal video patches. Our algorithm trains dictionaries for the calculation of a non-negative sparse representation for feature vectors and uses a linear Support Vector Machine (SVM) to distinguish between different actions. We evaluate the performance of the proposed techniques by using two human action datasets (KTH and IXMAS). In both cases, the proposed technique outperforms state-of-the-art techniques, achieving 100% accuracy on the KTH dataset.
A study on the HEVC performance over lossy networks

The high level of compression achieved by HEVC raises a question regarding its error-resilience performance extremely compressed data over an imperfect medium. In this paper, we study the behavior of this newly developed video coding standard over diverse networking scenarios. It is shown that HEVC is less error resilient compared to H.264/AVC, especially in the scenes with high amount of motion, in exchange of a significantly reduced bandwidth.
3D scalable lossless compression of medical images based on global and local symmetries

We recently proposed a symmetry-based scalable lossless compression method for 3D medical images using the 2D integer wavelet transform and the embedded block coder with optimized truncation (EBCOT). In this paper, we present two major contributions that enhance our early work: 1) a new block-based intra-band prediction method that exploits the global and local symmetries of the wavelet-transform sub-bands based on the main axis of symmetry as detected using the analytical Fourier-Mellin transform; and 2) a new inter-slice DPCM prediction method that exploits the correlation between slices. Performance evaluations on real 3D medical images show an average improvement of up to 17% in lossless compression ratios when compared to the state-of-the-art compression methods including 3D-JPEG2000, JPEG2000 and H.264 intra-coding.
3D video quality metric for 3D video compression

As the evolution of multiview display technology is bringing glasses-free 3DTV closer to reality, MPEG and VCEG are preparing an extension to HEVC to encode multiview video content. View synthesis in the current version of the 3D video codec is performed using PSNR as a quality metric measure. In this paper, we propose a full-reference Human-Visual-System based 3D video quality metric to be used in multiview encoding as an alternative to PSNR. Performance of our metric is tested in a 2-view case scenario. The quality of the compressed stereo pair, formed from a decoded view and a synthesized view, is evaluated at the encoder side. The performance is verified through a series of subjective tests and compared with that of PSNR, SSIM, MS-SSIM, VIFp, and VQM metrics. Experimental results showed that our 3D quality metric has the highest correlation with Mean Opinion Scores (MOS) compared to the other tested metrics.
A Link Adaptation Scheme for Efficient Transmission of H.264 Scalable Video Over Multirate WLANs

In this paper, we propose a cross-layer optimization scheme for delivery of scalable video over multirate wireless networks, in particular the popular 802.11 based wireless local area network (WLAN). The 802.11 based networks use a link adaptation mechanism in the physical layer (PHY) to maintain the reliability of transmission under varying channel conditions. When channel condition worsens, the reliability is maintained by employing more robust modulation and coding schemes, at the cost of reduced PHY bit rate. The reduced bit rate will result in lower available throughput for applications. For scalable video streaming applications, the conventional solution to this problem is to reduce the video bit rate by dropping the higher enhancement layers of the scalable video. We show in this article that the video quality can be improved, if the link adaptation scheme uses more intelligent reliability criteria and adjusts the PHY parameters used for delivering each video layer, according to the relative importance of that layer. Our scheme achieves better video quality without increasing the traffic load of the WLAN. For this purpose we present temporal fairness constraints and formulate an optimization problem for assigning different PHY modes to different layers of scalable video; the solution to this problem provides a set of PHY configuration parameters that achieve the highest possible video quality while meeting the admission control constraints in the network. Performance evaluations demonstrate that our method outperforms the existing mechanisms.
Chroma scaling for high dynamic range video compression

Color pixel encoding optimizes the conversion of linear physical values of light into integer values. The efficiency of such encoding methods depends on a trade-off between the bit-depth used and the visible distortion introduced by quantization. This efficiency for different color pixel encoding approaches has been evaluated in literature, without considering the fact that before transmission to the end-user, color encoded content needs to be compressed using a video codec. Thus, to be efficient, a color pixel encoding scheme needs not only to achieve the lowest bit-depth, but also to allow for efficient video compression ratio. Yet, when compressing dark video sequences, the most efficient color pixel encoding scheme known as Y'DuDv requires much higher bit-rates, hence negating its high encoding efficiency. In this article, we propose a chroma scaling technique that adaptively restricts the bit-depth of the chroma channels for optimized encoding of High Dynamic Range content. Results show that the proposed scaling reduces Y'DuDv bit-rate requirements for dark content while preserving its high color accuracy.
Subjective study on asymmetric stereoscopic video with low-pass filtered slices

This paper proposes a new method for asymmetric filtering of stereoscopic video. In traditional asymmetric stereoscopic video coding, the quality of one of the views is reduced while the other view is of original quality. However, this approach is not fair for people with one dominant eye. We propose to address this problem by reducing the quality of horizontal slices in both views. In our approach, we applied low-pass filtering to slices of both views while the corresponding slice in the other view is untouched. Subjective tests show that the quality, sharpness and depth of the low-passed filtered stereo video are close to the original one. We tested a different number of horizontal slices and various levels of filtering for two representative video sequences. Results show that low-pass filtering of the horizontal slices of both views with smoothing on the slice edges is an effective technique for asymmetric stereoscopic videos.
An Efficient Compression Scheme for Colour Filter Array Video Sequences

Most consumer digital cameras use a single light sensor which captures colour information using a colour filter array (CFA). This produces a mosaic image, where each pixel contains either a red, green or blue sample. The two missing colours at each pixel location must be interpolated from the surrounding samples in a process called demosaicking. The conventional approach to compressing video in these devices is to first perform demosaicking and then compress the resulting full-colour video using standard methods. In this paper a method for compressing CFA video prior to demosaicking is proposed, using a modified version of the H.264 video coding standard. Simulations show the proposed method gives better compression efficiency than the demosaick-first approach at high bit-rates
Rate and Distortion Modeling of CGS Coded Scalable Video Content

In this paper, we derive single layer and scalable video rate and distortion models for video bitstreams encoded using the coarse grain quality scalability (CGS) feature of the scalable extension of H.264/AVC. In these models, we assume the source is Laplacian distributed and compensate for errors in the distribution assumption by linearly scaling the Laplacian parameter . Moreover, we present simplified approximations of the derived models that allow for a run-time calculation of sequence dependent model constants. Our models use the mean absolute difference (MAD) of the prediction residual signal and the encoder quantization parameter (QP) as input parameters. Consequently, we are able to estimate the residual MAD, bitrate, and distortion of a future video frame at any QP value and for both base-layer and CGS layer packets. We also present simulation results that demonstrate the accuracy of the proposed models.
Novel Lossless fMRI Image Compression Based on Motion Compensation and Customized Entropy Coding

We recently proposed a method for lossless compression of 4-D medical images based on the advanced video coding standard (H.264/AVC). In this paper, we present two major contributions that enhance our previous work for compression of functional MRI (fMRI) data: (1) a new multiframe motion compensation process that employs 4-D search, variable-size block matching, and bidirectional prediction; and (2) a new context-based adaptive binary arithmetic coder designed for lossless compression of the residual and motion vector data. We validate our method on real fMRI sequences of various resolutions and compare the performance to two state-of-the-art methods: 4D-JPEG2000 and H.264/AVC. Quantitative results demonstrate that our proposed technique significantly outperforms current state of the art with an average compression ratio improvement of 13%.
The challenge of DVD authoring

The evolution of DVD promises telecomputers will find their way into our living rooms: linked to a large flat screen which will be able to display HDTV, standard definition TV, video games, interactive movies from digital versatile discs (DVD), Internet, video telephony and computer graphics. For the first time, Hollywood studios and consumer electronic companies have formed an alliance to support the DVD technology which is critical for both sides. We address the complex process of DVD authoring.
Improving the HDTV picture performance under noisy transmission conditions

A simple yet effective method for data synchronization of HDTV (high-definition television) signals is introduced. The scheme reduces the number of occurrences of undetected errors and limits the effects of errors to blocks of 512 pixel areas instead of the 1024, 3072, and 5632 pixel areas proposed by other schemes. Undetected errors are prevented from propagating to adjacent areas of the picture, and their effects are localized to within the 32*16 superblock in which the errors occur. This method does not change the compression rate while lowering the SNR at which picture loss occurs by 2.5 to 3 dB. This improvement is irrespective of the rate of the Reed-Solomon coding rate used by the HDTV system.<<ETX>>
Conversion of H.264-encoded 2D video to 3D format

An efficient 2D to 3D video conversion method that utilizes the H.264 motion estimation procedure is proposed. The depth information is determined based on the objects' motion information within the scene. Performance evaluations show that our approach outperforms the other existing H.264-based depth map estimation technique by up to 2.2 dB PSNR, and provides more realistic depth information about the scene. Subjective comparisons also confirm the superiority of our method.