Depending　on　high　quality　images,　industrial　vision　technologies　can　basically　oversee　all　the　industrial　production　processes,　such　as　workpiece　processing　and　assembly　automation,　which　play　a　highly　significant　role　in　promoting　detection　automation　and　production　capacity　in　assembly　lines.　Unlike　the　natural　scene　images　which　consist　of　richer　colors　and　natural　lines,　industrial　images　that　cover　complex　industrial　goods　and　equipment　are　made　up　of　fewer　colors,　more　regular　shapes,　massive　graphic　elements,　etc.,　causing　existing　image　processing　methods　for　quality　estimation,　enhancement　and　monitoring　to　fail.　Human　beings　usually　play　the　part　of　the　final　receiver　of　an　industrial　image,　so　in　the　researches　of　image　quality　estimation,　it　is　necessary　to　take　the　perception　process　of　human　eyes　and　brain　to　the　input　images　into　consideration.　On　this　basis,　we　in　this　paper　propose　a　novel　perceptual　information　fidelity　based　image　quality　estimation　model,　abbreviated　as　PIF.　Particularly,　we　first　introduce　a　visual-cell　low-pass　filter　and　an　optical-nerve　noise　model,　which　are　separately　inspired　by　the　two　processes:　one　is　that　an　image　in　the　form　of　optical　signals　arrives　at　the　retina　through　the　eye＇s　optical　system　to　form　the　stimuli;　the　other　is　that　the　aforesaid　stimuli　in　the　form　of　electrical　signals　transfer　to　the　human　brain　through　the　optical　nerve.　Second,　we　construct　a　novel　image　content-aware　adjustor　to　optimize　the　above　visual-cell　low-pass　filter　and　optical-nerve　noise　model.　Third,　we　compare　the　two　quantities　of　the　information　that　is　present　in　the　clean　image　and　how　much　of　the　information　can　be　extracted　from　the　lossy　image　to　generate　the　overall　quality　score.　Experiments　on　the　two　large-size　industrial　image　quality　databases　demonstrate　the　excellent　performance　achieved　by　our　proposed　PIF　model,　with　a　remarkable　performance　gain　over　the　existing　state-of-the-art　competitors.