Abstract
In this paper, a new, low-complexity, easy-to-implement hardware method for color space conversion between the red-green-blue (RGB) and the hue-saturation-intensity (HSI) color spaces called the simple RGB-HSI space conversion (S-SC) algorithm is proposed, which aims to provide more rapid computing due to the need for fewer operations. In the S-SC algorithm, we reconstruct the model of space conversion between the RGB color space and the HSI color space (RGB-HSI) by inverting the conversion from the HSI color space to the RGB color space of the traditional geometric derivation algorithm. As a result, the nonlinear model-realized RGB-HSI color space conversion by the geometric derivation algorithm is transformed into a linear conversion model, which can avoid complicated calculations such as trigonometric and inverse trigonometric functions in the color space conversion process. The model can effectively reduce the computational complexity of the algorithm and facilitate hardware implementation at the same time. To evaluate the performance of the S-SC algorithm, we first compare the S-SC algorithm with the geometric derivation algorithm from the computational complexity perspective. On this basis, we compare the S-SC algorithm with five other RGB-HSI color space conversion algorithms from the perspectives of error and conversion effect. Finally, we use the field programmable gate array (FPGA) hardware platform to analyze and verify the timing sequence and logical resource consumption and verify the effectiveness of the proposed algorithm with experimental results. We show that the S-SC algorithm achieves good performance in terms of conversion accuracy, logical unit resource occupancy, and output timing.
Highlights
Color spaces are essential in research fields such as image processing and computer vision
By reconstructing the mapping relationship between the R, G, and B components in the RGB color space and the H, S, and I components in the HSI color space, the nonlinear calculation in the traditional color space conversion algorithm is avoided, which reduces the computational complexity and improves the conversion efficiency
We investigate the performance of the simple RGB-HSI space conversion (S-SC) algorithm on the Lena image process together with five other RGB-HSI color space conversion algorithms for comparison
Summary
Color spaces are essential in research fields such as image processing and computer vision. These nonlinear calculations result in high algorithmic complexity and low conversion efficiency For this reason, based on the geometric derivation algorithm, researchers have proposed a variety of improved RGB-HSI conversion algorithms. The experimental results showed that the reconstructed RGB image, VOLUME 8, 2020 obtained by color space conversion of RGB-HSI by utilizing FPGA hardware, was almost the same as the original image It had the advantage of fast parallel data processing and maintained a high conversion accuracy. In the process of RGB-HSI color space conversion, nonlinear calculations frequently exist, such as trigonometric functions and inverse trigonometric functions These complicated nonlinear calculations, if not optimized, will consume a large number of logic resources that are not abundant in FPGA hardware. The pseudocode of the HSI2RGB conversion of the geometric derivation algorithm is shown in Algorithm 2
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