In industrial vision systems， subjective assessment is costly， pre-training for no-reference quality evaluation is time-intensive， and there is a critical need for a highly accurate full-reference image quality assessment model. To address these challenges， this study proposes a novel full-reference image quality assessment model based on singular value decomposition （SVD） with weighted texture information. First， SVD is applied to the reference image blocks， and the singular values of the distorted blocks are estimated using the singular vectors of both the reference and distorted image blocks， yielding the brightness similarity component. Next， the estimated singular values of the distorted image blocks are used to quantify average offset distortion and contrast change distortion， resulting in the contrast similarity component. The structural similarity of the images is then determined by analyzing the deviation of the singular vectors of the distorted image blocks from the unit matrix of the reference image blocks. Finally， the brightness， contrast， and structural similarity components are weighted using texture information to construct the full-reference image quality assessment model. The proposed method was evaluated on six widely used image quality assessment databases across four performance criteria. Experimental results demonstrate that the model achieves a weighted Spearman rank correlation coefficient of 0.896 3 across the datasets. For contrast change distortion， the model attains a Spearman rank correlation coefficient of 0.859 5， outperforming the second-best method by 85%. Compared to existing full-reference image quality assessment models， the proposed approach offers significant advantages in prediction accuracy， generalization capability， and computational efficiency.