PEA265: Perceptual Assessment of Video Compression Artifacts

The most widely used video encoders share a common hybrid coding framework that includes block-based motion estimation/compensation and block-based transform coding. Despite their high coding efficiency, the encoded videos often exhibit visually annoying artifacts, denoted as Perceivable Encoding Artifacts (PEAs), which significantly degrade the visual Qualityof- Experience (QoE) of end users. To monitor and improve visual QoE, it is crucial to develop subjective and objective measures that can identify and quantify various types of PEAs. In this work, we make the first attempt to build a large-scale subjectlabelled database composed of H.265/HEVC compressed videos containing various PEAs. The database, namely the PEA265 database, includes 4 types of spatial PEAs (i.e. blurring, blocking, ringing and color bleeding) and 2 types of temporal PEAs (i.e. flickering and floating). Each containing at least 60,000 image or video patches with positive and negative labels. To objectively identify these PEAs, we train Convolutional Neural Networks (CNNs) using the PEA265 database. It appears that state-of-theart ResNeXt is capable of identifying each type of PEAs with high accuracy. Furthermore, we define PEA pattern and PEA intensity measures to quantify PEA levels of compressed video sequence. We believe that the PEA265 database and our findings will benefit the future development of video quality assessment methods and perceptually motivated video encoders.Comment: 10 pages,15 figures,4 table

Multi-Spatio-temporal Fusion Graph Recurrent Network for Traffic forecasting

Traffic forecasting is essential for the traffic construction of smart cities in the new era. However, traffic data's complex spatial and temporal dependencies make traffic forecasting extremely challenging. Most existing traffic forecasting methods rely on the predefined adjacency matrix to model the Spatio-temporal dependencies. Nevertheless, the road traffic state is highly real-time, so the adjacency matrix should change dynamically with time. This article presents a new Multi-Spatio-temporal Fusion Graph Recurrent Network (MSTFGRN) to address the issues above. The network proposes a data-driven weighted adjacency matrix generation method to compensate for real-time spatial dependencies not reflected by the predefined adjacency matrix. It also efficiently learns hidden Spatio-temporal dependencies by performing a new two-way Spatio-temporal fusion operation on parallel Spatio-temporal relations at different moments. Finally, global Spatio-temporal dependencies are captured simultaneously by integrating a global attention mechanism into the Spatio-temporal fusion module. Extensive trials on four large-scale, real-world traffic datasets demonstrate that our method achieves state-of-the-art performance compared to alternative baselines

An inverse problem for semilinear equations involving the fractional Laplacian

Our work concerns the study of inverse problems of heat and wave equations involving the fractional Laplacian operator with zeroth order nonlinear perturbations. We recover nonlinear terms in the semilinear equations from the knowledge of the fractional Dirichlet-to-Neumann type map combined with the Runge approximation and the unique continuation property of the fractional Laplacian.Comment: 25 page

Semantic Face Compression for Metaverse: A Compact 3D Descriptor Based Approach

In this letter, we envision a new metaverse communication paradigm for virtual avatar faces, and develop the semantic face compression with compact 3D facial descriptors. The fundamental principle is that the communication of virtual avatar faces primarily emphasizes the conveyance of semantic information. In light of this, the proposed scheme offers the advantages of being highly flexible, efficient and semantically meaningful. The semantic face compression, which allows the communication of the descriptors for artificial intelligence based understanding, could facilitate numerous applications without the involvement of humans in metaverse. The promise of the proposed paradigm is also demonstrated by performance comparisons with the state-of-the-art video coding standard, Versatile Video Coding. A significant improvement in terms of rate-accuracy performance has been achieved. The proposed scheme is expected to enable numerous applications, such as digital human communication based on machine analysis, and to form the cornerstone of interaction and communication in the metaverse.Comment: 5 pages, 3 figure

A Framework on Complex Matrix Derivatives with Special Structure Constraints for Wireless Systems

Matrix-variate optimization plays a central role in advanced wireless system designs. In this paper, we aim to explore optimal solutions of matrix variables under two special structure constraints using complex matrix derivatives, including diagonal structure constraints and constant modulus constraints, both of which are closely related to the state-of-the-art wireless applications. Specifically, for diagonal structure constraints mostly considered in the uplink multi-user single-input multiple-output (MU-SIMO) system and the amplitude-adjustable intelligent reflecting surface (IRS)-aided multiple-input multiple-output (MIMO) system, the capacity maximization problem, the mean-squared error (MSE) minimization problem and their variants are rigorously investigated. By leveraging complex matrix derivatives, the optimal solutions of these problems are directly obtained in closed forms. Nevertheless, for constant modulus constraints with the intrinsic nature of element-wise decomposability, which are often seen in the hybrid analog-digital MIMO system and the fully-passive IRS-aided MIMO system, we firstly explore inherent structures of the element-wise phase derivatives associated with different optimization problems. Then, we propose a novel alternating optimization (AO) algorithm with the aid of several arbitrary feasible solutions, which avoids the complicated matrix inversion and matrix factorization involved in conventional element-wise iterative algorithms. Numerical simulations reveal that the proposed algorithm can dramatically reduce the computational complexity without loss of system performance

Conjunctival Lymphangiogenesis Was Associated with the Degree of Aggression in Substantial Recurrent Pterygia

Objective. To examine conjunctival lymphatic vessels and to analyze the relationship between lymphangiogenesis and aggressive recurrent pterygia. Methods. Tissues from 60 excised recurrent (including 19 of Grade 1, 28 of Grade 2, and 13 of Grade 3) pterygia were used in the study. Tissues from 9 nasal epibulbar conjunctivae segments were used as controls. Pterygium slices from each patient were immunostained with LYVE-1 monoclonal antibodies to identify lymphatic microvessels in order to calculate the lymphovascular area (LVA), the lymphatic microvessel density (LMD), and the lymphovascular luminal diameter (LVL). The relationship between lymphangiogenesis (LVA, LMD, and LVL) and pterygium aggression (width, extension, and area) was clarified. Results. Few LYVE-1 positive lymphatic vessels were found in the normal epibulbar conjunctiva segments. Lymphatic vessels were slightly increased in Grades 1 and 2 and were dramatically increased in Grade 3 recurrent pterygia. The LMD was correlated with the pterygium area in Grade 1 and 2 pterygia. In Grade 3, both LVA and LMD were significantly correlated with the pterygium area. Conclusions. Lymphangiogenesis was associated with the degree of aggression in recurrent pterygia, particularly in substantial Grade 3 recurrent pterygia