Description: Quantization error refers to the discrepancy between the actual value of a signal or image and the value obtained after the quantization process. In the context of digital signal processing and computer vision, this phenomenon occurs when an analog signal or image is converted into a digital representation. During this process, the continuous values of the signal or image are rounded to a finite set of discrete levels, which can result in the loss of information and detail. This error is particularly relevant in applications where precision and quality are critical, such as in telecommunications, imaging systems, and data compression. Quantization can lead to undesirable visual effects, such as ‘banding’, where smooth transitions become abrupt steps. Additionally, quantization error can influence the performance of various processing algorithms, affecting tasks such as filtering, compression, and other fundamental processes in digital signal and image processing. Therefore, understanding and mitigating quantization error is essential for improving the quality of digital representations and optimizing the performance of related systems.
