In　the　previous　work,　the　LoG　(Laplacian　of　Gaussian)　signal　that　is　the　earliest　stage　output　of　human　visual　neural　system　was　suggested　to　be　useful　in　image　quality　assessment　(IQA)　model　design.　This　work　considered　that　LoG　signal　carried　crucial　structural　information　of　IQA　in　the　position　of　its　zero-crossing　and　proposed　a　Non-shift　Edge　(NSE)　based　IQA　model.　In　this　study,　we　focus　on　another　aspect　of　the　properties　of　the　LoG　signal,　i.e.,　LoG　whitens　the　power　spectrum　of　natural　images.　Here　our　interest　is　that:　when　exposed　to　unnatural　images,　specifically　distorted　images,　how　does　the　HVS　whitening　this　type　of　signals?　In　this　paper,　we　first　investigate　the　whitening　filter　for　natural　image　and　distorted　image　respectively,　and　then　suggest　that　the　LoG　is　also　a　whitening　filter　for　distorted　images　to　some　extent.　Based　on　this　fact,　we　deploy　the　LOG　signal　in　the　task　of　IQA　model　design　by　applying　two　very　simple　distance　metrics,　i.e.,　the　MSE　(mean　square　error)　and　the　correlation.　The　proposed　models　are　analyzed　according　to　the　evaluation　performance　on　three　subjective　databases.　The　experimental　results　validate　the　usability　of　the　LoG　signal　in　IQA　model　design　and　that　the　proposed　models　stay　in　the　state-of-the-art　IQA　models.