Americas, Special Report, Health

[Aprpdltub]

First, gravity is a force that causes objects to attract one another. The simplest way to understand gravity is through Isaac Newton's law of universal gravitation. This law states that every particle in the universe attracts every other particle. The more massive an object is, the more strongly it attracts other objects. The closer objects are, the more strongly they attract each other. An enormous object, like the Earth, easily attracts objects that are close to it, like apples hanging from trees. Scientists haven't decided exactly what causes this attraction, but they believe it exists everywhere in the universe. Second, air is a fluid that behaves essentially the same way liquids do. Like liquids, air is made of microscopic particles that move in relation to one another. Air also moves like water does -- in fact, some aerodynamic tests take place underwater instead of in the air. The particles in gasses, like the ones that make up air, are simply farther apart and move faster than the particles in liquids.|We propose a method to compress full-resolution video sequences with implicit neural representations. Each frame is represented as a neural network that maps coordinate positions to pixel values. We use a separate implicit network to modulate the coordinate inputs, which enables efficient motion compensation between frames. Together with a small residual network, this allows us to efficiently compress P-frames relative to the previous frame. We further lower the bitrate by storing the network weights with learned integer quantization. Our method, which we call implicit pixel flow (IPF), offers several simplifications over established neural video codecs: it does not require the receiver to have access to a pretrained neural network, does not use expensive interpolation-based warping operations, and does not require a separate training dataset. We demonstrate the feasibility of neural implicit compression on image and video data. One reason for this is that neural codecs do not yet robustly outperform traditional codecs in terms of compression performance. At least as important, however, are practical considerations: neural codecs require access to a (typically large) neural network on each device on which videos need to be decompressed.




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