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Effective Gradient Descent-Based Chroma Subsampling Method for Bayer CFA Images in HEVC

2018; Institute of Electrical and Electronics Engineers; Volume: 29; Issue: 11 Linguagem: Inglês

10.1109/tcsvt.2018.2879095

1558-2205

Kuo‐Liang Chung, Yu-Ling Lee, Wei-Che Chien,

Image Enhancement Techniques

The most widely used color filter array (CFA) pattern in commercial digital color cameras is the Bayer pattern, and the captured image is called the Bayer CFA image, in which each pixel contains only one color value and each image consists of 25% red, 50% green and 25% blue color values. The chroma 4:2:2 or 4:2:0 subsampling of Bayer CFA images is a necessary process prior to compression. According to the block-distortion minimization principle, in this paper, we propose an effective gradient descent-based chroma subsampling (GDCS) method for Bayer CFA images. Based on the test Bayer CFA images collected from the Kodak and IMAX datasets, experimental results demonstrated that in high efficiency video coding, our GDCS method has better quality and quality-bitrate tradeoff performance of the reconstructed images when compared with the existing chroma subsampling methods.