This study presents a novel subjective image quality assessment for large samples to solve existing problems in subjective assessments of image quality databases, such as less distortion levels and insufficient analysis of experimental results. The proposed method is based on a double-stimulus continuous quality scale and employs a simplified, two-level subjective assessment scale. We obtain a quality sequence of sample images by integrating circularly, selecting the best quality, and adjusting the sequence. Then, fuzzy clustering is used to analyze the quality sequence. The probability of image quality sequence in fuzzy clustering analysis is taken as its matching degree, which establishes a fuzzy similarity matrix of samples. We obtain the image quality score by normalizing the probability, establishing the fuzzy similarity relationship, and building a fuzzy equivalence relation, classification, and scoring. We test the subjective assessment for a 64-distortion-level image. The results demonstrate that the image quality scores accurately reflect the variation of just-noticeable difference, assessment accuracy is up to 94%, standard deviation of the image quality scores is from 0 to 7, and the mean value of standard deviation is 3.08 (percentile system), which is much less than the current level of other image quality databases. The proposed method demonstrates high accuracy and stability, and is suitable for subjective assessments of image quality databases and the study of human visual characteristics.