An improved no-reference image quality assessment metric IQALE is proposed in this paper. Color space also includes lots of image information

and Lab color space is closer to human vision system. Therefore

to improve the metric accuracy

this paper adds a channel and b channel of Lab color space into the spatial-spectral entropy-based quality (SSEQ) algorithm. Information entropy is an image feature that is studied more in recent years

and can be applied to image quality assessment better. Information entropy is extracted in both color and gray spaces. Then the image features and MOS value are trained and tested via support vector machine (SVM). The results on LIVE

TID2008

MICT

CSIQ and IVC databases demonstrate that adding the information of Lab color space can improve the metric accuracy and IQALE algorithm is better than the recent popular no-reference image quality assessment algorithms. Moreover

in order to test the scalability of the proposed metric

the database independence experiment is conducted on the five image databases. According to the result

IQALE method has better and more stable accuracy by adding the feature of color entropy. The database independence experiment also shows the better Robustness of the method. Furthermore

IQALE has better universality for every distortion type.