Image　quality　assessment　aims　at　estimating　the　subject　quality　of　images　and　builds　models　to　high　efficiently　evaluate　the　perceptual　quality　of　the　image　for　many　applications.　Because　the　human　visual　system　(HVS)　is　highly　sensitive　to　structural　information,　various　image　features　have　been　studied　and　widely　applied　in　IQA　metrics　design.　Previous　work　has　validated　that　the　image　gradient　magnitude　and　the　Laplacian　of　Gaussian　(LOG)　operator　are　efficient　structural　features　in　IQA　tasks.　Most　of　the　IQA　metrics　work　capably　only　when　the　distorted　image　is　totally　registered　with　the　reference　image,　and　perform　poorly　on　images　even　with　small　translations.　In　this　paper,　we　suggested　an　FR-IQA　method　with　a　simple　combination　of　the　gradient　magnitude　and　the　LOG　signals,　which　obtains　satisfied　performance　in　evaluating　image　quality　while　considering　the　shift-invariance　property　for　not　well-registered　reference　and　distortion　image　pair.　Experimental　results　show　that　the　proposed　model　works　robustly　on　three　large　scale　subjective　IQA　databases　which　contain　a　variety　of　distortion　types　and　levels,　stays　in　the　state-of-the-art　FR-IQA　models　and　achieves　the　best　performance　in　terms　of　weighted　average　score　over　the　three　databases.　Furthermore,　we　proved　that　the　proposed　model　performs　better　in　translation-invariance　test　compared　with　the　competitors.　©　2019,　Springer　Nature　Switzerland　AG.