Abstract: The Non-Reference Image Quality Assessment (NRIQA) models based on Deep Learning(DL) include two common structures, namely the single-task structure and the multi-task structure. In order to study the effect of the multi-task structure on model accuracy without pre-training, the performance of the MEON-adjusted multi-task model and the single-task model in the NRIQA task is compared and analyzed. The accuracy rate of the multi-task model reaches 0.882 and 0.871 respectively on LIVE and TID2013 image quality assessment databases. Experiments show that the multi-task model without pre-training still exhibits equal even better performance than the single-task model. On this basis, the sub-task output dimension experiment of the multi-task model shows that in NRIQA research, the sub-task can be pre-trained on the relevant datasets according to the demand and goal, and then fine-tuned in combination with the quality evaluation task, it has the advantage of being able to integrate into other tasks by transferring learning.

Key words: deep learnimg(DL), non-reference image quality assessment (NRIQA), single-task model, multi-task model