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Three types of pictures (or frames) are used in video compression: I, P, and B frames.. An I‑frame (intra-coded picture) is a complete image, like a JPG or BMP image file.. A P‑frame (Predicted picture) holds only the changes in the image from a previous frame.
In inter-frame coding, individual frames of a video sequence are compared from one frame to the next, and the video compression codec records the differences to the reference frame. If the frame contains areas where nothing has moved, the system can simply issue a short command that copies that part of the previous frame into the next one.
This is a list of free and open-source software packages (), computer software licensed under free software licenses and open-source licenses.Software that fits the Free Software Definition may be more appropriately called free software; the GNU project in particular objects to their works being referred to as open-source. [1]
GStreamer is a pipeline-based multimedia framework that links together a wide variety of media processing systems to complete complex workflows. For instance, GStreamer can be used to build a system that reads files in one format, processes them, and exports them in another.
FFmpeg also includes other tools: ffplay, a simple media player, and ffprobe, a command-line tool to display media information. Among included libraries are libavcodec , an audio/video codec library used by many commercial and free software products, libavformat (Lavf), [ 8 ] an audio/video container mux and demux library, and libavfilter, a ...
Python uses sys.argv, e.g.: import sys for arg in sys . argv : print arg Python also has a module called argparse in the standard library for parsing command-line arguments.
An image segmentation neural network can process small areas of an image to extract simple features such as edges. [81] Another neural network, or any decision-making mechanism, can then combine these features to label the areas of an image accordingly. A type of network designed this way is the Kohonen map.
Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing.. The data is linearly transformed onto a new coordinate system such that the directions (principal components) capturing the largest variation in the data can be easily identified.