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LSQ, or Least Squares Quantization, is a method used in signal processing and data compression to approximate a continuous signal with a limited set of discrete values. It is a form of quantization that minimizes the sum of the squares of the differences between the original signal and the quantized signal. LSQ is often used in applications where it is important to accurately represent a signal with a limited number of quantization levels, such as in image and audio compression. By minimizing the error between the original signal and the quantized signal, LSQ can provide a high-quality representation of the original signal while efficiently using a limited amount of data. This makes it a valuable tool in various fields such as telecommunications, image processing, and data analysis.