We　propose　an　image　conspicuity　index　that　combines　three　factors:　spatial　dissimilarity,　spatial　distance　and　central　bias.　The　dissimilarity　between　image　patches　is　evaluated　in　a　reduced　dimensional　principal　component　space　and　is　inversely　weighted　by　the　spatial　separations　between　patches.　An　additional　weighting　mechanism　is　deployed　that　reflects　the　bias　of　human　fixations　towards　the　image　center.　The　method　is　tested　on　three　public　image　datasets　and　a　video　clip　to　evaluate　its　performance.　The　experimental　results　indicate　highly　competitive　performance　despite　the　simple　definition　of　the　proposed　index.　The　conspicuity　maps　generated　are　more　consistent　with　human　fixations　than　prior　state-of-the-art　models　when　tested　on　color　image　datasets.　This　is　demonstrated　using　both　receiver　operator　characteristics　(ROC)　analysis　and　the　Kullback-Leibler　distance　metric.　The　method　should　prove　useful　for　such　diverse　image　processing　tasks　as　quality　assessment,　segmentation,　search,　or　compression.　The　high　performance　and　relative　simplicity　of　the　conspicuity　index　relative　to　other　much　more　complex　models　suggests　that　it　may　find　wide　usage.