# Pixel Interaction Model for Contrast Enhancement: Bridging Social Science and Image Processing
## 4 Experimental Results
### Performance Evaluation
#### 4.2.1 Visual Performance  
The initial experiment utilizes the dataset introduced in [27], which comprises 22 low-contrast images and five histogram equalization-based algorithms: AVHEQ [19], BBHE [18], ESIHE [20], MMBEBHE [17], and RSSIHE [16], in addition to the proposed algorithm CONTRAST-OPINION. The results from each of the contrasting algorithms are compared in terms of the other elements used within this analysis, thereby giving the ability to visually compare the enhancements made to each image. The contrast enhancement experiment was conducted on the entire image dataset. However, due to space limitations, the results are presented for a subset of images. Six images (from \(I_{1}\) to \(I_{6}\)) were selected because they represent varying levels of difficulty based on their degree of low contrast. These six images, which illustrate different challenges in contrast enhancement, are shown in Figure 7.  
Figures 8-13 depict the generated images by each algorithm. The initial image in each figure represents the original low-contrast input image, and the subsequent images represent the best-processed images of each method employed: AVHEQ [19], BBHE [18], ESIHE [20], MMBEBEHE [17], and RSSIHE [16], as well as the proposed method, CONTRAST-OPINION. Figure 7: (\(I_{1}\)–\(I_{6}\)) input images with low contrast from the dataset TID2013. Figure 8: Results of the proposed technique for image \(I_{1}\) for (AVHEQ), (BBHE), (ESIHE), (MMBEBHE), (RSESIHE), and (C-OPINION) algorithms. Figure 9: _Cont._  
Figure 10: Results of the proposed technique for image \(I_{3}\) for (AVHEQ), (BBHE), (ESIHE), (MMBEBHE), (RSESIHE), and (C-OPINION) algorithms. Figure 9: Results of the proposed technique for image \(I_{2}\) for (AVHEQ), (BBHE), (ESIHE), (MMBEBHE), (RSESIHE), and (C-OPINION) algorithms. Figures 8-13 show that the proposed approach resulted in high-contrast images while still being able to achieve uniform enhancement without any visible distortion of the image. Indeed, C-OPINION reaches this by harmoniously balancing light and dark intensities and protecting characteristics and elements in each image, producing much more defined features that can be easily distinguished. In contrast, most algorithms often produce over-high or over-low intensities in the output image, sometimes with poor visual quality and even worse contrast as compared to the original. Therefore, in processing images with high contrast levels, the proposed method has distinguished itself for its superior capability. C-OPINION is a highly effective method for enhancing contrast in images. This technique has gained popularity due to its ability to improve intensity levels and produce exceptional results. Unlike other methods, which only work with the same number of intensity levels that the image has and perform a histogram stretch, attempting to cover levels from 0 to 255, C-OPINION increases the intensity levels, covering a broader range of grayscale levels across the entire histogram spectrum. This approach enables the improvement and identification of both large and fine details; hence, it leads to a better-defined structure of the image. Compared to the other contrast enhancement methods, C-OPINION varies in that it keeps the intensity levels and raises them, maintaining the visual char  
Figure 11: Results of the proposed technique for image \(I_{4}\) for (AVHEQ), (BBHE), (ESIHE), (MM-BEBHE), (RSESIHE), and (C-OPINION) algorithms. acteristics of the image with even sharper conditions. Additionally, this approach has the capability to produce various tonalities; hence, it is an effective technique of contrast enhancement compared to other methods that just perform a histogram stretch. Although the resulting images produced by the proposed approach surpass those generated by other methods in terms of contrast enhancement quality, it is crucial to conduct both qualitative and quantitative analyses.