The　screen　content　(SC)　pictures,　such　as　webpages,　serve　as　a　visible　and　convenient　medium　to　well-represent　the　Internet　information,　and　therefore,　the　visual　quality　of　SC　pictures　is　highly　significant　and　has　attained　a　growing　amount　of　attention.　Accurate　quality　evaluation　of　SC　pictures　not　only　provides　the　fidelity　of　the　conveyed　information,　but　also　contributes　to　the　improvement　of　the　user　experience.　In　practical　applications,　a　reliable　estimation　of　SC　pictures　plays　a　considerably　critical　role　for　the　optimization　of　the　processing　systems　as　the　guidance.　Based　on　these　motivations,　this　paper　proposes　a　novel　method　for　precisely　assessing　the　quality　of　SC　pictures　using　very　sparse　reference　information.　Specifically,　the　proposed　quality　method　separately　extracts　the　macroscopic　and　microscopic　structures,　followed　by　comparing　the　differences　of　macroscopic　and　microscopic　features　between　a　pristine　SC　picture　and　its　corrupted　version　to　infer　the　overall　quality　score.　By　studying　the　feature　histogram　for　dimensionality　reduction,　the　proposed　method　merely　requires　two　features　as　the　reference　information　that　can　be　exactly　embedded　in　the　file　header　with　very　few　bits.　Experiments　manifest　the　superiority　of　our　algorithm　as　compared　with　state-of-the-art　relevant　quality　metrics　when　applied　to　the　visual　quality　evaluation　of　SC　pictures.