Just Noticeable Difference (JND) and Satisfied User Ratio (SUR) has been widely investigated for compressed image and video to use the least resources (e.g., storage and bandwidth) without damaging the Quality of Experience (QoE) for end users. However, the current JND subjective test methodologies are extremely time consuming due to the large range of encoding parameters. Besides, the state-of-the-arts SUR/JND prediction models get non-negligible prediction error due to the limited masking effect features. To this end, we first proposed a preprocessing method to reduce the JND subjective test time by using dynamic range of encoding parameters and collected a new Video-Wise JND (VW-JND) datasets for HD videos: HD-VJND. Afterwards, based on the collected datasets, we proposed a SUR prediction framework by extracting 3 types of features 1) masking effect features; 2) bitstreams features; 3) content features. Feature selection is applied to extracted features before regression. Besides, we also compared the direct and indirect SUR value predictions methods. Experiment results shows that our proposed optimization can reduce 7.14% of the subjective experiment time compared to the widely used Robust Binary Search (RBS). Furthermore, the proposed SUR and JND prediction frameworks outperform the SOTA model in HD-VJND datasets.