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Image sampling is a fundamental technique for image compression, which greatly improves the efficiency of image storage, transmission, and applications. However, existing sampling algorithms primarily consider human visual perception and discard irrelevant information based on subjective preferences. Unfortunately, these methods may not adequately meet the demands of computer vision tasks and can even lead to redundancy because of the different preferences between human and computer. To tackle this issue, this paper investigates the key features of computer vision. Based on our findings, we propose an image sampling method based on the dominant color component (ISDCC). In this method, we utilize a grayscale image to preserve the essential structural information for computer vision. Then, we construct a concise color feature map based on the dominant channel of pixels. This approach provides relevant color information for computer vision tasks. We conducted experimental evaluations using well-known benchmark datasets. The results demonstrate that ISDCC adapts effectively to computer vision requirements, significantly reducing the amount of data needed. Furthermore, our method has a minimal impact on the performance of mainstream computer vision algorithms across various tasks. Compared to other sampling approaches, our proposed method exhibits clear advantages by achieving superior results with less data usage.

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A time-interleaved analog-to-digital converter (TIADC) system is a good option to significantly increase the sampling rate of an ADC. However, the performance of a TIADC suffers from mismatch errors among the sub-channels, especially the timing error. This paper presents a method to estimate the channel timing error by using the output data from TIADC and its corresponding reference channel. The proposed method introduces an estimate model based on the phase relationship at the non-overlapping frequency points. The only assumption we need is that the spectrum of input signal is sparse. The simulations show that the proposed method can estimate the timing error with high accuracy.

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