Mining discriminative spatial cues for aerial image quality assessment towards big data

Evaluating massive-scale aerial/satellite images quality is useful in computer vision and intelligent applications. Traditional local features-based algorithms have achieved impressive performance. However, spatial cues, i.e., geometric property and topological structure, have not been exploited effectively and explicitly. Thus, in this paper, we propose a novel method for image quality assessment towards aerial/satellite images, where discriminative spatial cues are well encoded. More specifically, in order to mine inherent spatial structure of aerial images, each image is segmented into several basic components such as buildings, airport and playground. Afterwards, a weighted region adjacency graph (RAG) is built based on the basic components to represent the spatial feature of each aerial image. We integrate the spatial feature with other transform domain features, and train a support vector regression model to achieve image quality assessment. Experiments demonstrate that our method shows competitive or even better performance compared with several state-of-the-art algorithms.     

An ensemble image quality assessment algorithm based on deep feature clustering

To effective handle image quality assessment (IQA) where the images might be with sophisticated characteristics, we proposed a deep clustering-based ensemble approach for image quality assessment toward diverse images. Our approach is based on a convolutional DAE-aware deep architecture. By leveraging a layer-by-layer pre-training, our proposed deep feature clustering architecture extracted a fixed number of high-level features at first. Then, it optimally splits image samples into different clusters by using the fuzzy C-means algorithm based on the engineered deep features. For each cluster, we simulated a particular fitting function of differential mean opinion scores with each assessed image’s PSNR, SIMM, and VIF scores. Comprehensive experimental results on TID2008, TID2013 and LIVE databases have demonstrated that compared to the state-of-the-art counterparts, our proposed IQA method can reflect the subjective quality of images more accurately by seamlessly integrating the advantages of three existed IQA methods.     

蓝牙耳机的主观评测方法

蓝牙无线耳机的使用主要分为音乐和通话两种场景。本文提出了一种主观评测音乐播放效果的方法,结果表明耳机的音乐播放效果都是很不错的。在通话测试部分,按照3GPP和沃达丰的测试规范进行上下行响度的测试,并对噪声消除进行了主观评测,结果表明,语音通话部分还有很大的改进空间。     

基于疫情下中职口罩自动化课程的快速搭建及线上学习探究

本文基于疫情下学校延期开学线上教学,以及疫情下行业自动化需求和发展趋势,快速整合了工作室和行业资源,搭建了中职口罩自动化课程框架,并对线上教学提出了应急授课方案和学习方法,明确了学习目标。实践研究表明,课程开发提高了线上教与学的有效性,又能实现学生核心素养培养目标。     

激光通信天线一体化的摆镜面形优化

一对多激光通信天线受结构和尺寸的限制,反射镜与安装平面只能单独研磨。虽然可根据面形测试结果对安装平面进行反复研磨,以保证摆镜安装后的面形精度,但该方法难度大、周期长、成功率低。为了减小加工难度,在保证正常研磨精度(5μm)的同时降低安装面平面度造成的安装应力和温度变形等外力影响,保证摆镜安装后的面形精度。研究了摆镜在力学作用下的面形精度变化和面形稳定性,优化设计了隔离结构,有效提高了摆镜的面形稳定性。测试结果表明,在测试波长为λ时,安装应力和温度升(降)载荷作用下摆镜面形精度的峰谷值由0.546λ提升到0.161λ,均方根值由0.099λ提升到0.019λ,完全符合设计要求。     

Blind image quality assessment with channel attention based deep residual network and extended LargeVis dimensionality reduction

Image Quality Assessment (IQA) is one of the fundamental problems in the fields of image processing, image/video coding and transmission, and so on. In this paper, a Blind Image Quality Assessment (BIQA) approach with channel attention based deep Residual Network (ResNet)and extended LargeVis dimensionality reduction is proposed. Firstly, ResNet50 with channel attention mechanism is used as the backbone network to extract the deep features from the image. In order to reduce the dimensionality of the deep features, LargeVis, which is originally designed for the visualization of large scale high-dimensional data, is extended by using Support Vector Regression (SVR) to perform on a single feature vector data. The extended LargeVis can remove the redundant information of the deep features so as to obtain a low-dimensional and discriminative feature representation. Finally, the quality prediction model is established by using SVR as the fitting method. The low-dimensional feature representation and quality score of the image form the pair-wise data samples to train the fitting model. Experimental results on authentic distortions datasets and synthetic distortions datasets show that our proposed method can achieve superior performance compared with the state-of-the-art methods.     

Biocompatible Blends of an Intrinsically Conducting Polymer as Stretchable Strain Sensors for Real-Time Monitoring of Starch-Based Food Processing

Because stretchable strain sensors that have a resistance or capacitance sensitive to strain can sense skin deformation during physical movement, they have been extensively studied as wearable devices for healthcare monitoring. In principle, they can be used to monitor starch-based food processing in real time, since starch-based food can have remarkable volume change during processing. Monitoring starch-based food processing in real time can help achieve high quality and high productivity while reducing energy consumption. Nevertheless, there is no such report in the literature. Here, a blend of starch and a biocompatible intrinsically conducting polymer, poly(3,4-ethylenedioxythiophene): polystyrenesulfonate (PEDOT:PSS), is reported as a stretchable strain sensor to monitor starch-based food processing including fermentation, steaming, storage, and refreshing in a real-time manner. The resistance of the blends increases during the food volume expansion mainly caused by the fermentation, steaming, and refreshing, and it decreases as a result of the food volume shrinkage during cooling or storage. The signals can be thus used to optimize the processing conditions and control the food quality. This technology can be easily combined with the Internet of Things.     

失效电子元器件分析方法

介绍了一种电子元器件失效分析方法,给出了失效器件失效的统计要素,并对失效要素进行分析、研究失效模式与失效机理,找出失效原因,找到生产过程中的薄弱环节,制定相应措施,及时有效预防器件的再次失效,提高电子元器件的使用可靠性,进而提高整机可靠性,以较小的质量成本获取较高的经济效益,避免产品出现重复性问题,最终达到控制质量成本的目的。     

思维导图在高等数学教学中的应用及方法探究

思维导图通过图文结合的方式,将高等数学知识点制成完整的结构图,以数学逻辑思维来梳理高等数学基础理论知识。文章主要从教学实践活动入手,发挥学生主体性,引导学生成为课堂活动的主体,按照自己的思维习惯和认知习惯,借助信息技术,构建多元化的思维导图。以此降低高等数学教学的难度,提升教学质量。     

Unified quality assessment of natural and screen content images via adaptive weighting on double scales

In multimedia communication systems, digital images may contain various visual contents, among which the natural scene images (NSIs) and screen content images (SCIs) are two important and common types. The existing full-reference image quality assessment (IQA) metrics are designed for only one type of images, but cannot precisely perceive the visual quality of another type. It is still unclear what the different characteristics are between NSIs and SCIs resulting in this failure. Inspired by some psychological studies, we figure out that it is due to the different structural scale levels between NSIs and SCIs. Given this observation, this paper introduces the gradient degradation of Gaussians (GDoG) to analyze the images’ structural scale level, proposes a fast unified IQA index for both NSIs and SCIs by incorporating an adaptive weighting strategy on double scales. Experimental results conducted on several databases verify the effectiveness and efficiency of the proposed unified IQA index for both types of images, also demonstrate that the adaptive weighting strategy based on GDoG can improve the existing models for cross-content-type images.