During　recent　years,　quality-aware　features　extracted　from　natural　scene　statistics　(NSS)　models　have　been　used　in　development　of　blind　image　quality　assessment　(BIQA)　algorithms.　Generally,　the　univariate　distributions　of　bandpass　coefficients　are　used　to　fit　a　parametric　probabilistic　model　and　the　model　parameters　serve　as　the　quality-aware　features.　However,　the　inter-location,　inter-direction　and　inter-scale　correlations　of　natural　images　cannot　be　well　exploited　by　such　NSS　models,　as　it　is　hard　to　capture　such　dependencies　using　univariate　marginal　distributions.　In　this　paper,　we　build　a　novel　NSS　model　of　joint　log-contrast　distribution　to　take　into　account　the　across　space　and　direction　correlations　of　natural　images　(inter-scale　correlation　to　be　explored　as　the　next　step).　Furthermore,　we　provide　a　new　efficient　approach　to　extract　quality-aware　features　as　the　gradient　of　log-likelihood　on　the　NSS　model,　instead　of　using　model　parameters　directly.　Finally,　we　develop　an　effective　joint-NSS　model　based　BIQA　metric　called　BJLC　(BIQA　based　on　joint　log-contrast　statistics).　Extensive　experiments　on　four　public　large-scale　image　databases　have　validated　that　objective　quality　scores　predicted　by　the　proposed　BIQA　method　are　in　higher　accordance　with　subjective　ratings　generated　by　human　observers　compared　with　existing　methods.　(C)　2018　Published　by　Elsevier　B.V.