论宏观复杂性与知觉编码
On Macroscopic Complexity and Perceptual Coding
摘要：
“有损”数据压缩算法的理论极限被认为是。宏观观察者所看到的物体的复杂性是感知代码的大小，它丢弃了所有可能丢失的信息，而不会改变特定观察者的感知。这种宏观观察到的状态的复杂性是对构成宏观状态的任何微观状态的最简单描述。基于宏观状态而不是微观状态复杂性的推理和模式识别将利用宏观观察者的复杂性来忽略不相关的噪音。
英文摘要：
The theoretical limits of 'lossy' data compression algorithms are considered. The complexity of an object as seen by a macroscopic observer is the size of the perceptual code which discards all information that can be lost without altering the perception of the specified observer. The complexity of this macroscopically observed state is the simplest description of any microstate comprising that macrostate. Inference and pattern recognition based on macrostate rather than microstate complexities will take advantage of the complexity of the macroscopic observer to ignore irrelevant noise.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
