A Cubic Convolution Interpolation-Based Chroma Subsampling Method for Bayer and RGBW CFA Raw Images
Kuo‐Liang Chung, Tsz-Ching Leung, Tzu-Yi Liu, Ya-Chi Tseng · IEEE Access · 2022
The Bayer and RGBW color filter array (CFA) raw images, denoted by$I^{Bayer}$and$I^{RGB}$, respectively, have been widely used in consumer markets. In the demosaicking-first compression scheme, chroma subsampling is necessary prior to compressing$I^{RGB}$and$I^{RGBW}$. Several linear interpolation-based chroma subsampling methods have been developed for$I^{Bayer}$, but no nonlinear interpolation-based chroma subsampling methods have targeted the above two CFA image types simultaneously. In this paper, we first propose a nonlinear interpolation-based, namely the cubic convolution interpolation-based (CCI-based),$2\times 2$block-distortion function for each$2\times 2$CFA block$B^{CbCr}$. Next, using the Cauchy-Schwarz inequality, we prove that the proposed block-distortion function is a convex function in the real domain, which further serves as the base of the initial subsampled chroma solution. Then, a CCI-based iterative method is proposed to improve the initial subsampled chroma solution. The results of comprehensive experimental tests using Bayer and RGBW CFA images created from the three RGB full-color datasets, namely IMAX, SCI (screen content images), and CI (classical images), demonstrate that on the Versatile Video Coding (VVC) platform VTM-11.0, the proposed method achieves substantial quality enhancement and quality-bitrate tradeoff merits of the reconstructed Bayer and RGBW CFA images compared with existing chroma subsampling methods.