China
Africa
Explore chapters and articles related to this topic
Image Compression
Published in Vipin Tyagi, Understanding Digital Image Processing, 2018
In comparison to JPEG, PNG format is better when the image has large, uniformly colored areas. PNG format also supports partial transparency that is useful in a number of applications, such as fades and anti-aliasing for text. PNG is a good choice for web browsers and can be fully streamed with a progressive display option.Tagged Image File Format (TIFF): TIFF is a flexible image file format that supports both lossless or lossy image compression. TIFF specification given by Aldus Corporation in 1986 normally saves eight bits per color (red, green, blue) for 24-bit images, and sixteen bits per color for 48-bit images. For handling images and data within a single file, TIFF file format has the header tags (size, definition, image-data arrangement, applied image compression) defining the image’s geometry. Thus, the details of the image storage algorithm are included as part of the file. TIFF format is widely accepted as a photograph file standard in the printing business, scanning, fax machines, word processing, optical character recognition, image manipulation and page-layout applications. TIFF files are not suitable for display on web due to their large size and most web browsers do not support these files.Bitmap (BMP): BMP file format by Microsoft is an uncompressed, simple, widely accepted format.
Making the Best Use of Displays
Published in Lars-Ingemar Lundström, Digital Signage Broadcasting, 2013
Some picture file formats, such as PNG, can have transparent areas. This means that such a file can be easily put on top of any screen content (Figure 3.11). The PNG format is very handy in digital signage because it is suitable for creating transparent layers. Transparent layers can be used to add different logos or temporary messages on top of any other graphics without disturbing the background content too much.
Interact with Me
Published in Chris Jackson, Flash Cinematic Techniques, 2012
The sky and clouds artwork was saved as a JPEG file. The middle ground was saved as a PNG file. PNG files retain the transparency of the alpha channel. After being imported into Flash, the artwork was converted into a movie clip and positioned on the appropriate layer in the Timeline.
Simplified Prediction Method for Detecting the Emergency Braking Intention Using EEG and a CNN Trained with a 2D Matrices Tensor Arrangement
Published in International Journal of Human–Computer Interaction, 2023
Hermes J. Mora, Esteban J. Pino
The TensorFlow (TF) algorithm is a Python-open source library for numerical calculus making machine learning faster and easier (Python, 2019). The usage of TF to design and train CNNs is reasonably easy due to the strong support in Artificial Intelligence that has a vast number of functions to manage the input data. We can build a data generator with the (.numpy) extension that corresponds to a Python library implemented for working with N-dimensional arrays. With this in mind, the input data can be used directly as a large array configured by matrices. It is no necessary a dataset as RGB or grayscale images. Implementing a tensor (n-dimensional matrix) implies that the network designer reduces the processing time and computer resources when training the network. Based on Table 2, the dataset for each of the six electrode groups is configured directly as a (.numpy) array, Figure 3(b). By contrast, the image groups used to train our CNN through grayscale images and compare the CNN results are converted into the common image file format (.png). There are six electrode groups (4, 8, 13, 18, 33, 59) that result in six different 2 D-tensors. The height of each 2 D matrix varies according to the number of electrodes. The width of the matrix (400 samples) because there is no variation in the length of segments. The number of images in each image-set is significantly reduced given the number of electrodes used in each group.
An Efficient Internet Map Tiles Rendering Approach on High Resolution Devices
Published in Journal of Spatial Science, 2023
Mingqiang Guo, Liang Wu, Ying Huang, Xueye Chen
Recent years have witnessed the emergence of CyberGIS (Wang et al. 2013), a new-generation GIS based on the synthesis of advanced cyberinfrastructure, geographic information science (GIScience) and spatial analysis and modelling. GIScience has been applied in many fields, such as building pattern recognition (Bei et al. 2019), building extraction (Guo and Liu et al. 2020), spatial analysis (Guo and Han et al., 2021; Guo and Song et al. 2021), big data visualization (Guo et al. 2015), etc. An online map service is one of the fundamental functions of CyberGIS. Two typical kinds of map services exist. One is the Tile map service, such as Google Map, Bing Map, Yahoo Map, Baidu Map, etc. Tile maps are pre-generated and stored as image files (e.g. jpg, png and gif), and the map server returns the target tile images to the clients in response to their requests. The other one is the vector map service, which is usually used for visualizing vector data that is updated frequently. For example, a CyberGIS service may allow users to visualize the resultant maps generated by a series of online processes. The map server must query, retrieve and render the vector data, and generate the map image on the fly, whenever it receives a visualization request from a client. However, such an on-demand visualization approach can be very time-consuming when large volumes of data are used (Guo and Guan et al. 2015). Therefore, the tile map service is more appropriate for internet map service providers.
The construction of virtual simulation platform for pingtan experimental area based on HTML5 and WebGL
Published in Enterprise Information Systems, 2020
Teaching aid data integration technology includes the following aspects: (1) different teaching aid data are classified, and local teaching aid data and network teaching aid data are set, respectively, according to the data form. (2) the local auxiliary data and network auxiliary data are classified into two levels. For example, the local auxiliary data can be divided into documents, pictures, video and other types. (3) According to the different types of the local auxiliary data, format classification is conducted, respectively, such as document format of doc, PDF, PPT, XLS, TXT, etc.; image formats of JPG, PNG, raw, GIF, SWF, etc. Picture format of rm, avi, mp4, MKV, WMV, etc. (4) the local auxiliary data and network auxiliary data in different data forms are stored into the MySQL database at the back end of the platform, and the integration expression is finally realised.