As　machine　learning　continues　to　improve　the　performance　of　image　compression,　there　is　a　high　demand　for　deep　learning-based　image　compression　algorithms.　The　first　generation　of　deep　learning-based　image　compression　standard,　JPEG　AI,　has　emerged.　Compared　to　the　linear　transform　methods　in　traditional　compression　frameworks,　deep　learning-based　image　compression　codecs　use　non-linear　transform　to　extract　visual　features　ranging　from　low　to　high　levels　in　a　large　number　of　training　samples,　thereby　achieving　much　higher　compression　performance.　JPEG　AI　aims　to　explore　image　encoding　methods　that　are　more　efficient　than　existing　image　codecs.　In　the　JPEG　AI　official　verification　model,　the　Content　Adaptive　Inter-Channel　Correlation　Information　(ICCI)　subnetwork　is　used　to　reconstruct　compressed　images　to　achieve　higher　quality,　but　the　complexity　and　parameter　number　of　this　subnetwork　are　relatively　high.　To　solve　this　problem,　we　propose　a　simplified　ICCI　(sICCI)　based　on　the　Y,　U,　and　V　components.　Compared　to　the　standard　ICCI　module　in　JPEG　AI　and　its　lightweight　version　eICCI,　our　proposed　sICCI　significantly　reduces　network　complexity　and　model　parameters　while　keeping　competitive　image　reconstruction　quality.　©　2024　SPIE.