A 3D lookup table (3D LUT) is a classic yet effective tool for image enhancement and restoration tasks, even in the deep learning era. The 3D LUT efficiently reduces model size and runtime by instantly transforming an input color value into another color value through interpolation of pre-calculated values at the vertices. However, a limitation of 3D LUT transforms is their lack of spatial information, as they convert color values on a point-by-point basis. To address this weakness, researchers have explored spatial-aware 3D LUT methods, which provide spatial features through additional modules. While spatial-aware 3D LUT methods show promising performance, the extra modules introduce a substantial number of parameters and an increased runtime, particularly as the resolution of the input image rises. To tackle this issue, we propose a method for generating image-adaptive 3D LUTs by considering the redundant parts of tables. We introduce an efficient framework that decomposes the 3D LUT into a linear sum of low-dimensional LUTs and utilizes singular value decomposition (SVD). Additionally, we modify the modules for spatial features to be more cache-efficient and image-adaptive, thereby reducing both runtime and improving performance. Our model effectively reduces the number of parameters and runtime, while maintaining competitive performance, as demonstrated by extensive experimental results.