The kernel extreme learning machine (KELM) is more robust and has a faster learning speed when compared with the traditional neural networks, and thus it is increasingly gaining attention in hyperspectral image (HSI) classification. Although the Gaussian radial basis function kernel widely used in KELM has achieved promising classification performance in supervised HSI classification, it does not consider the underlying data structure of HSIs. In this paper, we propose a novel spectral-spatial KELM method (termed as MF-KELM) by incorporating the mean filtering kernel into the KELM model, which can properly compute the mean value of the spatial neighboring pixels in the kernel space. Considering that in the situation of limited training samples the classification result is very noisy, the spatial bilateral filtering information on spectral band-subsets is introduced to improve the accuracy. Experiment results show that our method outperforms other kernel functions based on KELM in terms of classification accuracy and visual comparison.

Over the past 20 years, research on quality of experience (QoE) has been actively expanded even to cover aesthetic, emotional and psychological experiences. QoE has been an important research topic in determining the perceptual factors that are essential to users in keeping with the emergence of new display technologies. In this paper, we provide in-depth reviews of recent assessment studies in this field. Compared to previous reviews, our research examines the human factors observed over various recent displays and their associated assessment methods. In this study, we first provide a comprehensive QoE analysis on 2D display including image/video quality assessment (I/VQA), visual preference, and human visual system-related studies. Second, we analyze stereoscopic 3D (S3D) QoE research on the topics of I/VQA and visual discomfort from the human perception point of view on S3D display. Third, we investigate QoE in a head-mounted display-based virtual reality (VR) environment, and deal with VR sickness and 360 I/VQA with their individual approach. All of our reviews are analyzed through comparison of benchmark models. Furthermore, we layout QoE works on future display and modern deep-learning applications.

In this paper, we present a technique that is known as Residual Sign Prediction in Transform Domain (TDRSP) and is aimed at increasing compression performance by reducing the bits overhead of residue sign. These signs are typically coded by entropy coders in bypass mode that results in the high cost of sign bins which require 1 bit per bin in a bitstream. TDRSP allows us to reduce this cost by predicting residue signs so that the probability of a guess is significantly higher than 50%. Hence, arithmetic coding with contexts becomes applicable to the signs. In contrast to Residual Sign Prediction (RSP) performed in spatial domain, TDRSP avoids switching between domains and carries out calculations completely in transform domain to efficiently decrease the computational complexity of RSP. Simulations performed on top of Versatile Video Coding test model reference software (VTM-1.0) in accordance with the Joint Video Experts Team common test conditions show that more than 2.0% and up to 1.8% of the Bjøntegaard Delta rate can be achieved for All Intra and Random Access configurations, respectively.

It has been well studied that reliable multicast enables consistency protocols, including Byzantine Fault Tolerant protocols, for distributed systems. However, no transport-layer reliable multicast is used today due to limitations with existing switch fabrics and transport-layer protocols. In this paper, we introduce a layer-4 (L4) transport based on remote direct memory access (RDMA) datagram to achieve reliable multicast over a shared optical medium. By connecting a cluster of networking nodes using a passive optical cross-connect fabric enhanced with wavelength division multiplexing, all messages are broadcast to all nodes. This mechanism enables consistency in a distributed system to be maintained at a low latency cost. By further utilizing RDMA datagram as the L4 protocol, we have achieved a low-enough message loss-ratio (better than one in 68 billion) to make a simple Negative Acknowledge (NACK)-based L4 multicast practical to deploy. To our knowledge, it is the first multicast architecture able to demonstrate such low message loss-ratio. Furthermore, with this reliable multicast transport, end-to-end latencies of eight microseconds or less (< 8us) have been routinely achieved using an enhanced software RDMA implementation on a variety of commodity 10G Ethernet network adapters.

Deep learning has demonstrated its superiority in computer vision. Landsat images have specific characteristics compared with natural images. The spectral and texture features of the same class vary along with the imaging conditions. In this paper, we extend the use of deep learning to remote sensing image classification to large geographical regions, and explore a way to make deep learning classifiers transferable for different regions. We take Jingjinji region and Henan province in China as the study areas, and choose FCN, ResNet, and PSPNet as classifiers. The models are trained by different proportions of training samples from Jingjinji region. Then we use the trained models to predict results of the study areas. Experimental results show that the overall accuracy decreases when trained by small samples, but the recognition ability on mislabeled areas increases. All methods can obtain great performance when used to Jingjinji region while they all need to be fine-tuned with new training samples from Henan province, due to the reason that images of Henan province have different spectral features from the original trained area.

Recent trends in multimedia technologies indicate the need for richer imaging modalities to increase user engagement with the content. Among other alternatives, point clouds denote a viable solution that offers an immersive content representation, as witnessed by current activities in JPEG and MPEG standardization committees. As a result of such efforts, MPEG is at the final stages of drafting an emerging standard for point cloud compression, which we consider as the state-of-the-art. In this study, the entire set of encoders that have been developed in the MPEG committee are assessed through an extensive and rigorous analysis of quality. We initially focus on the assessment of encoding configurations that have been defined by experts in MPEG for their core experiments. Then, two additional experiments are designed and carried to address some of the identified limitations of current approach. As part of the study, state-of-the-art objective quality metrics are benchmarked to assess their capability to predict visual quality of point clouds under a wide range of radically different compression artifacts. To carry the subjective evaluation experiments, a web-based renderer is developed and described. The subjective and objective quality scores along with the rendering software are made publicly available, to facilitate and promote research on the field.

Recent progress in video compression is seemingly reaching its limits making it very hard to improve coding efficiency significantly further. The adaptive loop filter (ALF) has been a topic of interest for many years. ALF reaches high coding gains and has motivated many researchers over the past years to further improve the state-of-the-art algorithms. The main idea of ALF is to apply a classification to partition the set of all sample locations into multiple classes. After that, Wiener filters are calculated and applied for each class. Therefore, the performance of ALF essentially relies on how its classification behaves. In this paper, we extensively analyze multiple feature-based classifications for ALF (MCALF) and extend the original MCALF by incorporating sample adaptive offset filtering. Furthermore, we derive new block-based classifications which can be applied in MCALF to reduce its complexity. Experimental results show that our extended MCALF can further improve compression efficiency compared to the original MCALF algorithm.

In this paper we combine video compression and modern image processing methods. We construct novel iterative filter methods for prediction signals based on Partial Differential Equation (PDE)-based methods. The central idea of the signal adaptive filters is explained and demonstrated geometrically. The meaning of particular parameters is discussed in detail. Furthermore, thorough parameter tests are introduced which improve the overall bitrate savings. It is shown that these filters enhance the rate-distortion performance of the state-of-the-art hybrid video codecs. In particular, based on mathematical denoising techniques, two types of diffusion filters are constructed: a uniform diffusion filter using a fixed filter mask and a signal adaptive diffusion filter that incorporates the structures of the underlying prediction signal. The latter has the advantage of not attenuating existing edges while the uniform filter is less complex. The filters are embedded into a software based on HEVC with additional QTBT (Quadtree plus Binary Tree) and MTT (Multi-Type-Tree) block structure. Overall, the diffusion filter method achieves average bitrate savings of 2.27% for Random Access having an average encoder runtime increase of 19% and 17% decoder runtime increase. For UHD (Ultra High Definition) test sequences, bitrate savings of up to 7.36% for Random Access are accomplished.

In this paper, we compare the video codecs AV1 (version 1.0.0-2242 from August 2019), HEVC (HM and x265), AVC (x264), the exploration software JEM which is based on HEVC, and the VVC (successor of HEVC) test model VTM (version 4.0 from February 2019) under two fair and balanced configurations: All Intra for the assessment of intra coding and Maximum Coding Efficiency with all codecs being tuned for their best coding efficiency settings. VTM achieves the highest coding efficiency in both configurations, followed by JEM and AV1. The worst coding efficiency is achieved by x264 and x265, even in the placebo preset for highest coding efficiency. AV1 gained a lot in terms of coding efficiency compared to previous versions and now outperforms HM by 24% BD-Rate gains. VTM gains 5% over AV1 in terms of BD-Rates. By reporting separate numbers for JVET and AOM test sequences, it is ensured that no bias in the test sequences exists. When comparing only intra coding tools, it is observed that the complexity increases exponentially for linearly increasing coding efficiency.

Due to the increased popularity of augmented (AR) and virtual (VR) reality experiences, the interest in representing the real world in an immersive fashion has never been higher. Distributing such representations enables users all over the world to freely navigate in never seen before media experiences. Unfortunately, such representations require a large amount of data, not feasible for transmission on today's networks. Thus, efficient compression technologies are in high demand. This paper proposes an approach to compress 3D video data utilizing 2D video coding technology. The proposed solution was developed to address the needs of “tele-immersive” applications, such as VR, AR, or mixed reality with “Six Degrees of Freedom” capabilities. Volumetric video data is projected on 2D image planes and compressed using standard 2D video coding solutions. A key benefit of this approach is its compatibility with readily available 2D video coding infrastructure. Furthermore, objective and subjective evaluation shows significant improvement in coding efficiency over reference technology. The proposed solution was contributed and evaluated in international standardization. Although it is was not selected as the winning proposal, as very similar solution has been selected developed since then.