Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-prediction

The exorbitant increase in the computational complexity of modern video coding standards, such as High Efficiency Video Coding (HEVC), is a compelling challenge for resource-constrained consumer electronic devices. For instance, the brute force evaluation of all possible combinations of available coding modes and quadtree-based coding structure in HEVC to determine the optimum set of coding parameters for a given content demand a substantial amount of computational and energy resources. Thus, the resource requirements for real time operation of HEVC has become a contributing factor towards the Quality of Experience (QoE) of the end users of emerging multimedia and future internet applications. In this context, this paper proposes a content-adaptive Coding Unit (CU) size selection algorithm for HEVC intra-prediction. The proposed algorithm builds content-specific weighted Support Vector Machine (SVM) models in real time during the encoding process, to provide an early estimate of CU size for a given content, avoiding the brute force evaluation of all possible coding mode combinations in HEVC. The experimental results demonstrate an average encoding time reduction of 52.38%, with an average Bjøntegaard Delta Bit Rate (BDBR) increase of 1.19% compared to the HM16.1 reference encoder. Furthermore, the perceptual visual quality assessments conducted through Video Quality Metric (VQM) show minimal visual quality impact on the reconstructed videos of the proposed algorithm compared to state-of-the-art approaches

SDP-Based Quality Adaptation and Performance Prediction in Adaptive Streaming of VBR Videos

Recently, various adaptation methods have been proposed to cope with throughput fluctuations in HTTP adaptive streaming (HAS). However, these methods have mostly focused on constant bitrate (CBR) videos. Moreover, most of them are qualitative in the sense that performance metrics could only be obtained after a streaming session. In this paper, we propose a new adaptation method for streaming variable bitrate (VBR) videos using stochastic dynamic programming (SDP). With this approach, the system should have a probabilistic characterization along with the definition of a cost function that is minimized by a control strategy. Our solution is based on a new statistical model where the future streaming performance is directly related to the past bandwidth statistics. We develop mathematical models to predict and develop simulation models to measure the average performance of the adaptation policy. The experimental results show that the prediction models can provide accurate performance prediction which is useful in planning adaptation policy and that our proposed adaptation method outperforms the existing ones in terms of average quality and average quality switch

Elderly Fall Detection Systems: A Literature Survey

Falling is among the most damaging event elderly people may experience. With the ever-growing aging population, there is an urgent need for the development of fall detection systems. Thanks to the rapid development of sensor networks and the Internet of Things (IoT), human-computer interaction using sensor fusion has been regarded as an effective method to address the problem of fall detection. In this paper, we provide a literature survey of work conducted on elderly fall detection using sensor networks and IoT. Although there are various existing studies which focus on the fall detection with individual sensors, such as wearable ones and depth cameras, the performance of these systems are still not satisfying as they suffer mostly from high false alarms. Literature shows that fusing the signals of different sensors could result in higher accuracy and lower false alarms, while improving the robustness of such systems. We approach this survey from different perspectives, including data collection, data transmission, sensor fusion, data analysis, security, and privacy. We also review the benchmark data sets available that have been used to quantify the performance of the proposed methods. The survey is meant to provide researchers in the field of elderly fall detection using sensor networks with a summary of progress achieved up to date and to identify areas where further effort would be beneficial

Fusion features ensembling models using Siamese convolutional neural network for kinship verification

Family is one of the most important entities in the community. Mining the genetic information through facial images is increasingly being utilized in wide range of real-world applications to facilitate family members tracing and kinship analysis to become remarkably easy, inexpensive, and fast as compared to the procedure of profiling Deoxyribonucleic acid (DNA). However, the opportunities of building reliable models for kinship recognition are still suffering from the insufficient determination of the familial features, unstable reference cues of kinship, and the genetic influence factors of family features. This research proposes enhanced methods for extracting and selecting the effective familial features that could provide evidences of kinship leading to improve the kinship verification accuracy through visual facial images. First, the Convolutional Neural Network based on Optimized Local Raw Pixels Similarity Representation (OLRPSR) method is developed to improve the accuracy performance by generating a new matrix representation in order to remove irrelevant information. Second, the Siamese Convolutional Neural Network and Fusion of the Best Overlapping Blocks (SCNN-FBOB) is proposed to track and identify the most informative kinship clues features in order to achieve higher accuracy. Third, the Siamese Convolutional Neural Network and Ensembling Models Based on Selecting Best Combination (SCNN-EMSBC) is introduced to overcome the weak performance of the individual image and classifier. To evaluate the performance of the proposed methods, series of experiments are conducted using two popular benchmarking kinship databases; the KinFaceW-I and KinFaceW-II which then are benchmarked against the state-of-art algorithms found in the literature. It is indicated that SCNN-EMSBC method achieves promising results with the average accuracy of 92.42% and 94.80% on KinFaceW-I and KinFaceW-II, respectively. These results significantly improve the kinship verification performance and has outperformed the state-of-art algorithms for visual image-based kinship verification

Algorithms and methods for video transcoding.

Video transcoding is the process of dynamic video adaptation. Dynamic video adaptation can be defined as the process of converting video from one format to another, changing the bit rate, frame rate or resolution of the encoded video, which is mainly necessitated by the end user requirements. H.264 has been the predominantly used video compression standard for the last 15 years. HEVC (High Efficiency Video Coding) is the latest video compression standard finalised in 2013, which is an improvement over H.264 video compression standard. HEVC performs significantly better than H.264 in terms of the Rate-Distortion performance. As H.264 has been widely used in the last decade, a large amount of video content exists in H.264 format. There is a need to convert H.264 video content to HEVC format to achieve better Rate-Distortion performance and to support legacy video formats on newer devices. However, the computational complexity of HEVC encoder is 2-10 times higher than that of H.264 encoder. This makes it necessary to develop low complexity video transcoding algorithms to transcode from H.264 to HEVC format. This research work proposes low complexity algorithms for H.264 to HEVC video transcoding. The proposed algorithms reduce the computational complexity of H.264 to HEVC video transcoding significantly, with negligible loss in Rate-Distortion performance. This work proposes three different video transcoding algorithms. The MV-based mode merge algorithm uses the block mode and MV variances to estimate the split/non-split decision as part of the HEVC block prediction process. The conditional probability-based mode mapping algorithm models HEVC blocks of sizes 16×16 and lower as a function of H.264 block modes, H.264 and HEVC Quantisation Parameters (QP). The motion-compensated MB residual-based mode mapping algorithm makes the split/non-split decision based on content-adaptive classification models. With a combination of the proposed set of algorithms, the computational complexity of the HEVC encoder is reduced by around 60%, with negligible loss in Rate-Distortion performance, outperforming existing state-of-art algorithms by 20-25% in terms of computational complexity. The proposed algorithms can be used in computation-constrained video transcoding applications, to support video format conversion in smart devices, migration of large-scale H.264 video content from host servers to HEVC, cloud computing-based transcoding applications, and also to support high quality videos over bandwidth-constrained networks