With　the　rapid　development　of　3D　capture　technologies,　point　cloud　has　been　widely　used　in　many　emerging　applications　such　as　augmented　reality,　autonomous　driving,　and　3D　printing.　However,　point　cloud,　used　to　represent　real　world　objects　in　these　applications,　may　contain　millions　of　points,　which　results　in　huge　data　volume.　Therefore,　efficient　compression　algorithms　are　essential　for　point　cloud　when　it　comes　to　storage　and　real-time　transmission　issues.　Specially,　the　attribute　compression　of　point　cloud　is　still　challenging　owing　to　the　sparsity　and　irregular　distribution　of　corresponding　points　in　3D　space.　In　this　paper,　we　present　a　novel　point　cloud　attribute　compression　scheme　based　on　inter-prediction　of　blocks　and　graph　Laplacian　transforms　for　attributes　residual.　Firstly,　we　divide　the　entire　point　cloud　into　adaptive　sub-clouds　via　K-means　based　on　the　geometry　to　acquire　sub-clouds,　which　enables　efficient　representation　with　less　cost.　Secondly,　the　sub-clouds　are　divided　into　two　parts,　one　is　the　attribute　means　of　the　sub　clouds,　another　is　the　attribute　residual　by　removing　the　means.　For　the　attribute　means,　we　use　inter-prediction　between　sub-clouds　to　remove　the　attribute　redundancy,　and　the　attribute　residual　is　encoded　after　graph　Fourier　transforming.　Experimental　results　demonstrate　that　the　proposed　scheme　is　much　more　efficient　than　traditional　attribute　compression　schemes.　©　2022　SPIE.