Learned Quality Enhancement via Multi-Frame Priors for HEVC Compliant Low-Delay Applications

Networked video applications, e.g., video conferencing, often suffer from poor visual quality due to unexpected network fluctuation and limited bandwidth. In this paper, we have developed a Quality Enhancement Network (QENet) to reduce the video compression artifacts, leveraging the spatial and temporal priors generated by respective multi-scale convolutions spatially and warped temporal predictions in a recurrent fashion temporally. We have integrated this QENet as a standard-alone post-processing subsystem to the High Efficiency Video Coding (HEVC) compliant decoder. Experimental results show that our QENet demonstrates the state-of-the-art performance against default in-loop filters in HEVC and other deep learning based methods with noticeable objective gains in Peak-Signal-to-Noise Ratio (PSNR) and subjective gains visually

High Linearity Broadband RF Vector Multiplier for Analog/RF Pre-distortion

Wireless communication systems are moving towards a heterogeneous solution, where small-cell base stations such as pico-cells and femto-cells are used concurrently with macro- cell base stations in high data traffic areas. Small-cell networks are expected to provide much larger wireless data rates and capacity in small areas while only consuming a fraction of the power. However, power amplifier nonlinearity does not scale down with the size of the base station; a similar degree of nonlinearity correction is required in both small-cell and macro-cell base stations, meaning that the power consumed by the signal linearization circuits is the same. An analog-radio frequency pre-distortion (ARF-PD) solution, operating at a fraction of a conventional digital pre-distortion's power consumption, has been proposed to support the unrestrained growth of wireless communication. This thesis forms part of an ongoing research project aimed at developing a fully integrated ARF-PD solution - a promising, low-power alternative to digital pre-distortion for future wireless communications. Specifically, it focuses on delivering an integrated design of a low-power high-linearity broadband radio frequency (RF) vector multiplier, which can be used as part of the ARF-PD solution. An RF vector multiplier is considered one of the major function blocks in analog pre-distortion solutions, as it allows the analog pre-distorter to interface with the undistorted signal in the RF domain. In the thesis, two RF vector multiplier designs are proposed and implemented in integrated circuits. In the first implementation, the RF vector multiplier is designed to directly apply pre-distortion to the RF signal. This architecture imposes a need for high gain in the RF vector multiplier, which results in large transistor size and high power consumption in the output stage. The design is able to achieve promising simulation results, however, performance limitations and disadvantages are also clearly exposed compared to commercial products. To resolve the issues discovered, an alternative ARF-PD architecture is adopted to relax the output power level needed from the RF vector multiplier. In addition, a self-linearized variable gain amplifier topology is proposed to improve system linearity. Overall, the second design shows significant improvement in bandwidth, linearity and output noise level, while only consuming half of the power consumed by the first design. Ultimately, simulation results have shown satisfying performance for both RF vector multipliers as part of an ARF-PD system. However, both of the proposed integrated circuit designs should be validated by measurement

The Cauchy problem of the Camassa-Holm equation in a weighted Sobolev space: Long-time and Painlev\'e asymptotics

Based on the $\overline\partial$-generalization of the Deift-Zhou steepest descent method, we extend the long-time and Painlev\'e asymptotics for the Camassa-Holm (CH) equation to the solutions with initial data in a weighted Sobolev space $H^{4,2}(\mathbb{R})$. With a new scale $(y,t)$ and a RH problem associated with the initial value problem,we derive different long time asymptotic expansions for the solutions of the CH equation in different space-time solitonic regions. The half-plane $\{ (y,t): -\infty 0\}$ is divided into four asymptotic regions: 1. Fast decay region, $y/t \in(-\infty,-1/4)$ with an error $\mathcal{O}(t^{-1/2})$; 2. Modulation-solitons region, $y/t \in(2,+\infty)$, the result can be characterized with an modulation-solitons with residual error $\mathcal{O}(t^{-1/2 })$; 3. Zakhrov-Manakov region,$y/t \in(0,2)$ and $y/t \in(-1/4,0)$. The asymptotic approximations is characterized by the dispersion term with residual error $\mathcal{O}(t^{-3/4})$; 4. Two transition regions, $|y/t|\approx 2$ and $|y/t| \approx -1/4$, the results are describe by the solution of Painlev\'e II equation with error order $\mathcal{O}(t^{-1/2})$.Comment: 61 page

Transient asymptotics of the modified Camassa-Holm equation

We investigate long time asymptotics of the modified Camassa-Holm equation in three transition zones under a nonzero background. The first transition zone lies between the soliton region and the first oscillatory region, the second one lies between the second oscillatory region and the fast decay region, and possibly, the third one, namely, the collisionless shock region, that bridges the first transition region and the first oscillatory region. Under a low regularity condition on the initial data, we obtain Painlev\'e-type asymptotic formulas in the first two transition regions, while the transient asymptotics in the third region involves the Jacobi theta function. We establish our results by performing a $\bar{\partial}$ nonlinear steepest descent analysis to the associated Riemann-Hilbert problem.Comment: 58 pages, 16 figures. Comments are welcom

Evaluating Point Cloud Quality via Transformational Complexity

Full-reference point cloud quality assessment (FR-PCQA) aims to infer the quality of distorted point clouds with available references. Merging the research of cognitive science and intuition of the human visual system (HVS), the difference between the expected perceptual result and the practical perception reproduction in the visual center of the cerebral cortex indicates the subjective quality degradation. Therefore in this paper, we try to derive the point cloud quality by measuring the complexity of transforming the distorted point cloud back to its reference, which in practice can be approximated by the code length of one point cloud when the other is given. For this purpose, we first segment the reference and the distorted point cloud into a series of local patch pairs based on one 3D Voronoi diagram. Next, motivated by the predictive coding theory, we utilize one space-aware vector autoregressive (SA-VAR) model to encode the geometry and color channels of each reference patch in cases with and without the distorted patch, respectively. Specifically, supposing that the residual errors follow the multi-variate Gaussian distributions, we calculate the self-complexity of the reference and the transformational complexity between the reference and the distorted sample via covariance matrices. Besides the complexity terms, the prediction terms generated by SA-VAR are introduced as one auxiliary feature to promote the final quality prediction. Extensive experiments on five public point cloud quality databases demonstrate that the transformational complexity based distortion metric (TCDM) produces state-of-the-art (SOTA) results, and ablation studies have further shown that our metric can be generalized to various scenarios with consistent performance by examining its key modules and parameters