IEEE Trans Image Process. 2012 Aug;21(8):3339-52. doi: 10.1109/TIP.2012.2191563. Epub 2012 Mar 21.
We develop an efficient, general-purpose, blind/noreference image quality assessment (NR-IQA) algorithm using a natural scene statistics (NSS) model of discrete cosine transform (DCT) coefficients. The algorithm is computationally appealing, given the availability of platforms optimized for DCT computation. The approach relies on a simple Bayesian inference model to predict image quality scores given certain extracted features. The features are based on an NSS model of the image DCT coefficients. The estimated parameters of the model are utilized to form features that are indicative of perceptual quality. These features are used in a simple Bayesian inference approach to predict quality scores. The resulting algorithm, which we name BLIINDS-II, requires minimal training and adopts a simple probabilistic model for score prediction. Given the extracted features from a test image, the quality score that maximizes the probability of the empirically determined inference model is chosen as the predicted quality score of that image. When tested on the LIVE IQA database, BLIINDS-II is shown to correlate highly with human judgments of quality, at a level that is competitive with the popular SSIM index.
我们开发了一种高效、通用的盲/无参考图像质量评估（NR-IQA）算法，该算法使用离散余弦变换（DCT）系数的自然场景统计（NSS）模型。该算法具有吸引力的计算能力，因为有专门为 DCT 计算优化的平台。该方法依赖于一种简单的贝叶斯推理模型，根据某些提取的特征来预测图像质量分数。特征基于图像 DCT 系数的 NSS 模型。该模型的估计参数用于形成表示感知质量的特征。这些特征用于简单的贝叶斯推理方法来预测质量分数。由此产生的算法，我们称之为 BLIINDS-II，需要最小的训练，并采用简单的概率模型进行分数预测。给定测试图像的提取特征，选择最大化经验确定推理模型概率的质量分数作为该图像的预测质量分数。当在 LIVE IQA 数据库上进行测试时，BLIINDS-II 与人类对质量的判断高度相关，与流行的 SSIM 指数相当。
