Image color is an important property that contains essential information that can be utilized for conducting accurate image analyses as well as for searching and retrieving images from databases. The colors of an image may be distorted during image acquisition, transmission, and display due to a variety of factors including environmental conditions. Developing an effective and quantitative metric for evaluating the color quality of an image that agrees with human observers is challenging, yet essential for computer vision and autonomous imaging systems. The traditional colorfulness measures are not robust to noise and fail to distinguish different color tones. In this paper, a new nonreference color quality measure CQE, that combines a colorfulness measure and a Uni-Color Differentiation term is presented. This CQE is shown to satisfy the established properties of a good measure, namely: The CQE correlates well with the human perception, which means that the measure can evaluate the quality of images accurately compared to the human observer’s evaluation; The measure is robust to noise and distortions so that it can provide consistent and reliable measure values for a wide range of images; The measure is computationally efficient and can be used in real time applications. The experimental results demonstrate the effectiveness of the CQE measure in evaluating image color qualities for a variety of test images subjected to different environmental conditions, as well as showing its applicability for fast image retrieval for synthetic patches and natural images. Conducting image retrieval by simply searching for the value of the image’s CQE measure is fast, easy to implement, and invariant to image orientations.