Referenceless image quality assessment is a challenging and critical problem in today's multimedia applica\-tions. Texture patterns in images are normally at high frequencies compared to lower ones. Due to the effect of distortions during acquisition, compression, and transmission, texture deviation artifacts are generated that cause a granular effect in the image. Other artifacts, such as blocking, affect high frequencies in an image, causing distorted edges. Combining the analysis of texture deviation and other artifacts helps in determining the quality of an image. The proposed approach uses variation in the energy of pixels to quantify the quality of an image. These variations are calculated using texture energy measures and pattern strength-based statistical features. In the proposed approach, machine learning-based classifiers are used to predict the quality score for an image. The performance of the proposed method is tested for all images ranging from pristine to poor quality from LIVE and TID2008 databases. For different distortions, results are shown to have good correlation if they lie between the predicted score and the differential mean opinion score. Results obtained with this approach are compared with other widely used referenceless approaches. It is observed that the proposed approach shows better performance in the quantification of the quality of an image.