Image　colorization　has　a　wide　range　of　applications,　but　it　remains　a　challenging　task　due　to　it　is　an　inherently　ill-posed　problem　with　multi　-modal　uncertainty.　The　advancement　of　deep　learning　techniques　has　provided　extensive　avenues　for　addressing　image　colorization.　However,　current　works　mainly　suffer　from　two　problems:　inaccurate　colorization　leading　to　biased　color　tones　(e.g.,　cool　or　warm　bias)　and　undersaturation　of　images.　Existing　Transformer-based　methods　can　produce　impressive　results,　but　they　often　come　with　high　training　costs　and　may　result　in　color　overflow　effects.　In　this　paper,　we　propose　a　two-stage　image　colorization　strategy　based　on　a　color　codebook.　Clustering　methods　in　the　three-dimensional　CIE　Lab　color　space　is　proposed　to　integrate　brightness　information　so　that　the　colors　in　the　codebook　can　be　lifelike.　In　the　first　stage,　we　treat　the　colorization　task　as　a　classification　problem　based　on　a　color　codebook,　and　a　highquality　codebook　is　advantageous　for　enhancing　color　classification　accuracy.　In　the　second　stage,　different　from　the　traditional　Transformer-based　method,　a　pyramid-type　Transformer　structure　is　used　to　extract　rich　image　features　to　refine　the　colors,　which　can　solve　potential　color　bands,　color　errors　and　color　overflow.　In　addition,　the　parameters　and　FLOPs　are　significantly　smaller　than　other　traditional　Transformer-based　methods.　Extensive　experiments　demonstrate　that　our　method　outperforms　state　-of　-the　-art　approaches.　On　the　ImageNet　validation　set,　the　achieved　values　are　4.60,　25.23,　0.19,　and　39.82　in　terms　of　FID,　PSNR,　LPIPS,　and　CF,　respectively.　On　the　COCO-Stuff　validation　set,　the　achieved　values　are　5.62,　25.15,　0.19,　and　36.25　in　terms　of　FID,　PSNR,　LPIPS,　and　CF,　respectively.　The　codes　are　available　at　https://github.com/Tanghui2000/Twostage_Image_Colorization_via_Color_Codebook.