An improved quantum-inspired evolutionary algorithm for coding resource optimization based network coding multicast scheme

This paper investigates how to minimize the required coding resources in network-coding-based multicast scenarios. An evolutionary algorithm (MEQEA) is proposed to address the above problem. Based on quantum-inspired evolutionary algorithm (QEA), MEQEA introduces multi-granularity evolution mechanism which allows different chromosomes, at each generation, to have different rotation angle step values for update. In virtue of this mechanism, MEQEA significantly improves its capability of exploration and exploitation, since its optimization performance is no longer overly dependant upon the single rotation angle step scheme shared by all chromosomes. MEQEA also presents an adaptive quantum mutation operation which is able to prevent local search efficiently. Simulations are carried out over a number of network topologies. The results show that MEQEA outperforms other heuristic algorithms and is characterized by high success ratio, fast convergence, and excellent global-search capability.     

一种面向新一代可伸缩视频编码的上采样设计

可伸缩视频编码属于新一代H.265/HEVC视频编码标准中的关键技术。本文分析了其相关技术,针对传统空间可伸缩视频编码中的上采样过程运算复杂、时间延迟等缺点,借鉴双边滤波器,将内容自适应嵌入到上采样过程中,提出了一种新的非线性和内容自适应设计方法。通过理论推导得出了改进后滤波器的表示公式,并设计出了新的上采样实现流程。通过仿真实验验证了理论推导的正确性,并与传统方法进行了比较,结果表明该方法可有效地提高编码效率、降低系统复杂性。     

16kbit/s分带自适应预测语音压缩系统中的正交镜象滤波器

本文讨论了16kbit/s分带自适应(SB—APC)预测语音压缩编码系统中的正交镜象滤波器(QMF)的原理和设计过程,并给出了实现QMF的硬件和软件结构。     

信源平衡编码及其在流密码中的应用

本文对离散无记忆信源给出了一种编码方法，它使编码后信源输出有着最好的平衡性，即0与1的数目最大程度地接近．对等长编码，本文给出的方法是步优的；对基于Huffman码的平衡编码，在保持平均码长不变的情况下，本文给出的方法使0与1的平衡性达到最优．因为一般信源字符的频率是一个统计数字．这里的平衡性也是从统计角度来说的．最后我们还讨论了这种平衡编码对流密码中的非线性组合器在防止相关攻击方面的防护作用．     

Low complexity inter coding scheme for Versatile Video Coding (VVC)

The latest coding standard Versatile Video Coding (VVC) developed by the Joint Video Experts Team (JVET) and Video Coding Experts Group (VCEG) was finalized in 2020. By introducing several new coding techniques, VVC improves the compression efficiency by 50% compared with H.265/HEVC. However, the coding complexity increases dramatically, which obstructs it from real-time application. To tackle this issue, a fast inter coding algorithm utilizing coding information is proposed to speed up the coding process. First, by analyzing the coding areas of the neighboring CUs, we predict the coding area of the current CU to terminate unnecessary splitting modes. Then, the temporally optimal coding mode generated during the prediction process is further utilized to shrink the candidate modes to speed up the coding process. Finally, the distribution of neighboring prediction modes are exploited to measure the motion complexity of the current CU, based on which the unnecessary prediction modes can be skipped earlier. Experimental results demonstrate that the proposed method can reduce the coding complexity by 40.08% on average with 0.07 dB BDPSNR decrease and 1.56% BDBR increase, which outperforms the state-of-the-art approach.     

Multi-view representation learning for multi-view action recognition

Although multiple methods have been proposed for human action recognition, the existing multi-view approaches cannot well discover meaningful relationship among multiple action categories from different views. To handle this problem, this paper proposes an multi-view learning approach for multi-view action recognition. First, the proposed method leverages the popular visual representation method, bag-of-visual-words (BoVW)/fisher vector (FV), to represent individual videos in each view. Second, the sparse coding algorithm is utilized to transfer the low-level features of various views into the discriminative and high-level semantics space. Third, we employ the multi-task learning (MTL) approach for joint action modeling and discovery of latent relationship among different action categories. The extensive experimental results on M²I and IXMAS datasets have demonstrated the effectiveness of our proposed approach. Moreover, the experiments further demonstrate that the discovered latent relationship can benefit multi-view model learning to augment the performance of action recognition.     

A two-stage convolutional sparse prior model for image restoration

Image restoration (IR) from noisy, blurred or/and incomplete observed measurement is one of the important tasks in image processing community. Image prior is of utmost importance for recovering a high quality image. In this paper, we present a two-stage convolutional sparse prior model for efficient image restoration. The multi-view features prior is first obtained by convolving the image with the Fields-of-Experts (FoE) filters and then the resulting multi-view features are represented by convolutional sparse coding (CSC) prior. By taking advantage of the convolutional filters, the proposed two-stage model inherits the strengths of multi-view features and CSC priors. The assembled multi-view features contain high-frequency, redundancy, and large range of feature orientations, which are favor to be represented by CSC and consequently for better image recovery. Augmented Lagrangian and alternating direction method of multipliers are employed to decouple the nonlinear optimization problem in order to iteratively approach the optimum solution. The results of various experiments on image deblurring and compressed sensing magnetic resonance imaging (CS-MRI) reconstruction consistently demonstrate that the proposed algorithm efficiently recovers image and presents advantages over the current leading restoration approaches.     

Interleaved coding and sequential transmission scheme for packet‐level forward error correction over burst loss channels

Burst packet loss is a common problem over wired and wireless networks and leads to a significant reduction in the performance of packet‐level forward error correction (FEC) schemes used to recover packet losses during transmission. Traditional FEC interleaving methods adopt the sequential coding‐interleaved transmission (SCIT) process to encode the FEC packets sequentially and reorder the packet transmission sequence. Consequently, the burst loss effect can be mitigated at the expense of an increased end‐to‐end delay. Alternatively, the reversed interleaving scheme, namely, interleaved coding‐sequential transmission (ICST), performs FEC coding in an interleaved manner and transmits the packets sequentially based on their generation order in the application. In this study, the analytical FEC model is constructed to evaluate the performance of the SCIT and ICST schemes. From the analysis results, it can be observed that the interleaving delay of ICST FEC is reduced by transmitting the source packets immediately as they arrive from the application. Accordingly, an Enhanced ICST scheme is further proposed to trade the saved interleaving time for a greater interleaving capacity, and the corresponding packet loss rate can be minimized under a given delay constraint. The simulation results show that the Enhanced ICST scheme achieves a lower packet loss rate and a higher peak signal‐to‐noise‐ratio than the traditional SCIT and ICST schemes for video streaming applications.     

基于物理层网络编码的非对称中继系统性能分析

在无线通信研究中,网络编码因其可有效提升带宽利用率的特性得到了大量关注.但是,网络编码用于双向中继信道(TWRC)时,中继位置的不对称将造成系统性能的下降,故在源节点使用分层调制的方法来解决此问题.研究了分层调制和物理层网络编码的联合以及优化,并进行了系统仿真.仿真结果显示,在非对称中继信道下,通过与传统调制方案(CM...     

基于图像的浮雕雕刻自动编程软件的开发

为了实现浮雕的数控加工,提出了基于图像的浮雕雕刻方法。该软件采用数字图像处理技术,结合丰富的加工参数选项,实现了浮雕雕刻的自动编程。经测试其生成的数控加工程序在数控机床上能够加工出令人满意的浮雕作品,同时配合仿真软件的使用,可迅速获得浮雕的加工效果和加工时间,有助于产品的生产与加工安排。