Video quality assessment becomes increasingly important due to the many applications of video where the end user is a human. Therefore, it is desirable to develop a visual quality metric that6 correlates well with human visual perception. This project deals with image quality assessment technique that was developed in SIPL and is based on DCT Sub-bands Similarity (DSS). The proposed technique exploits important characteristics of human visual perception by measuring similarity in the Discrete Cosine Transform (DCT) domain. This project task was to tune and adjust the algorithm in order to get better results. The improvements were using a Gaussian sample window, measuring Euclidean distance instead of linear and optimizing the DC & AC constants values. Also we used a dynamic algorithm which weighted the sub-band groups according to the knee point the sub-band graph. The results were a small improvement in the CSIQ image database, and slight degradations on the LIVE & TID image databases.