An FPGA-Based Simple RGB-HSI Space Conversion Algorithm for Hardware Image Processing

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.

Uniformity Detection for Straws Based on Overlapping Region Analysis

“…Based on the weighted average method, the straw image in grayscale was obtained (Figure 4). Usually, HSV and HSI are powerful tools to distinguish targets, which can present the color intensity intuitively [30]. The hue and saturation are regarded as the main indices in such models.…”

“…To compare the identification accuracy, the HSV and HSI models were adopted to test the performance in this study, and the results are shown in Figure 5. Usually, HSV and HSI are powerful tools to distinguish targets, which can present the color intensity intuitively [30]. The hue and saturation are regarded as the main indices in such models.…”

An improved dark channel defogging algorithm based on the HSI colour space

“…Save the improved dark channel image I ′ recon . end for end for VIII: Convert the fog-free image from the HSI colour space to the RGB colour space: for x = 1:N Based on H recon , S recon and I ′ recon , the fog-free components R ′ (x), G ′ (x), and B ′ (x) can be calculated according to our previous work [25]. Save the reconstructed R ′ , G ′ and B ′ components of the RGB colour space.…”

“…Therefore, in order to obtain the best processing results, a variety of RGB-HSI colour space conversion methods are proposed, but the relevant methods cannot achieve an effective balance between complexity and conversion accuracy. To this end, we propose a fast RGB-HSI colour space conversion method (named S-SC algorithm), which can effectively reduce the complexity of the algorithm while ensuring the conversion accuracy [25].…”

“…Based on this, in order to obtain the best processing effect, one must solve the invalidation problem of the DCP algorithm when dealing with large-scale and high-brightness sky regions, improve the processing efficiency of the defogging algorithm, and meet the real-time processing requirements of images or video images; this paper put forward an improved dark channel defogging algorithm on the basis of our previous research work in [25]. First, the S-SC algorithm is used to convert the foggy image from the RGB space to the HSI space.…”