We use mathematical modeling methods to incorporate new elements into the computer graphics technology and visual communication design process. This method realizes the design of a computer image visual communication system. This method uses artificial vision to model and extracts local features. The micro-partial equation obtains the background reference line of the target point in the image sequence to complete the background suppression in the image spatio-temporal domain. The experimental results show that the proposed algorithm has a better tracking effect of subsequent frames of video images and higher tracking efficiency.

#### Keywords

- Computer
- visual communication
- mathematical modeling
- feature extraction

#### MSC 2010

- 60G35

Helping the visually impaired restore or improve vision has been a dream of humankind for many years. It is also the research direction that scientific and technological workers have been committed to for a long time. Among them, artificial vision compensation technology has made great progress in recent years [1]. Visual impairment is generally caused by damage to certain parts of the visual pathway, such as the retina. The basic method of artificial vision compensation is to implant a microchip in the optic nerve pathway to generate a certain electrical signal to stimulate the optic nerve cell. This causes its excitement to generate neural action potentials transmitted to the primary visual cortex and higher visual center to induce human visual perception. We propose a pixelated imaging model based on the generation of salient local features. The article designs a simulation evaluation experiment based on the subjective evaluation scoring method to analyze the performance of this model. The article hopes to provide a reference for follow-up research in this field.

The research results show that the original image has some local structural features that are of interest to visual institutions. They do not need to go through high-level visual behaviors such as classification, extraction, and recognition. These local areas with obvious attractive properties are “significant” areas. Its “inspiration” to the eyes gives people the feeling that the area is rich in visual information [2]. On the one hand, although the “salience area” has different structural forms, visual perception can all be noticed. On the other hand, there is a “side inhibition” effect between visual perception units, and there is a certain spatial distribution “receptive field” for the excitation signal. Based on this, a pixelated imaging model frame based on local saliency features is shown in Figure 1.

The processing flow of this model on the original input image includes:

(1) Use several saliency feature detectors to extract the original image's various local saliency structure maps. (2) Implement “competition” for each mapping to reproduce the enhancement and inhibition of the visual receptive field, and then “fusion” all feature maps to obtain the final saliency map. (3) We implement multi-resolution pixelization of the original image and sub-sampling to hundreds of pixels based on the final feature mapping. Areas with stronger salient features give finer resolution, and vice versa. Subjectively, strong saliency areas rich in visual information are given priority to the subjects to achieve the effect of delivering as much visual information as possible.

First, the contrast saliency map needs to be obtained. The contrast of a local area relative to the surrounding area can be quantitatively described by Michaelson contrast. The Michaelson contrast at pixel position (_{m}_{M}

Secondly, it is necessary to obtain the edge density saliency map. The edge density describes the strength of the effective edge in the unit area of the original image. First, we need to select a suitable edge extraction method to obtain meaningful edges and then calculate the edge strength in each region [3]. An operator that can effectively detect important edges, suppress noise, and accurately locate edges is the Canny operator. We give a Gaussian filter G and use the following formula to estimate the normal unit vector of the local edge

The location of the edge satisfies

The strength of the edge is equal to the denominator of equation (2). We threshold the detection results to remove weak responses and perform edge synthesis to obtain meaningful edges.

Then it is necessary to obtain the directional difference saliency mapping. The so-called directional difference can be illustrated in Figure 2(a). In this original image with texture attributes, most of the primitives are close to the horizontal direction. In the experiment, 4 primitives are different, so the area centered on these 4 primitives shows a difference in directionality with the surroundings [4]. When viewing this image, the line of sight will be attracted by 4 primitives different from the surroundings. Therefore, these areas with different directionality from the surrounding area belong to the salient feature area of interest. The directional difference detection operator we selected uses a 2-dimensional Gabor filter

We use a Gabor filter with an appropriate center frequency. The direction angles are 0°, 45°, 90°, and 135°. The test is performed in Figure 2(a), and the results are as shown in 2(b)–(e). The response effect in the 4 local areas of interest is always different from the surroundings. Combining the four response results can get the directional difference significance map is shown in Figure 2(f). The highlighting in the 4 local areas of interest indicates a significant difference in directionality with the surrounding area.

Finally, the symmetry saliency mapping needs to be obtained. Based on the literature, define the contribution of pixel positions (_{n}

The saliency mapping of the original image's contrast, edge density, directional difference, and symmetry feature can be obtained by the effects of the above-mentioned salient feature detectors on the original image.

When we use the detectors, as mentioned earlier, to act on the original image, there will always be a response to the non-uniform grayscale area in the image. Therefore, the entire saliency map will show strong and weak responses and scattered noise-like responses. The area of interest of the visual system is limited [5]. The imaging resolution of artificial vision systems is limited. Therefore, only certain areas where the saliency is concentrated and reach a certain intensity can be regarded as the truly significant feature areas. The competitive step is reserved for aggregated significant responses. This weakens other responses to get a limited salient area. If there is a characteristic excitation of unit intensity at (0, 0) and _{0}. The enhanced response felt at (

The inhibitory response felt at (

Based on the above expression, a comprehensive reaction expression can be obtained

We use this template to convolve with the salient feature maps. This can approximately characterize the enhancement and suppression effects of the visual system on these features. The parameter _{1}, _{1}, _{2}, _{2} determines the shape and size of the “competition” area. Laplacian of Gaussian template can be further used in the simulation of artificial vision compensation under subjective experiment

Only one parameter of this template needs to be adjusted. This is simple and easy to perform imaging based on subjective simulation experiments. The significance of A at (_{n}_{n}

_{n}

In the image segmentation algorithm, the splitting steps of the splitting and merging algorithm are easy to correspond to the imaging model with the phantom optical array as the target. The split and merge method needs to determine a uniformity criterion. If a square in the image does not meet this criterion, it is divided into 4 squares. The commonly used uniformity criterion is the gray-level variance threshold criterion. If the gray variance in a square is less than a certain threshold, it is considered uniform. The “uniformity” criterion is revised to the significant degree criterion [7]. The block whose significance is lower than a certain threshold is considered as no need to be divided, and the block is regarded as a phantom visual point. We use the average gray level in the square to represent the image point's imaging result; otherwise, we continue to divide it into four. Such a prominent square area will be presented to the observer at a higher resolution.

The first implementation is a single feature experiment. Its purpose is to pass the test of each feature of contrast, edge density, directivity difference, and symmetry one by one. We examine their effects on visual perception functions. This provides a basis for selecting the weight coefficient of the feature fusion equation (11).

We divide the original image to be imaged into four scene category groups: the human face, ordinary object, indoor scene, and outdoor scene. Each group includes 12 256×256 grayscale images. Each image of each group extracts the salient features of contrast, edge density, directional difference, and symmetry according to the imaging model [8]. Then complete the “competition” of features. Since the object of investigation is a single feature, we control the result of each split to be as close to 400 small blocks (pixels) as possible. In this way, each original image gets 4 imaging results that reflect each salient feature. They become the subject of evaluation.

Nine university undergraduates and graduate students participated in the experiment as subjects. The subjects directly observed the imaging results with the naked eye from the computer display screen and compared them with the original image. The subjects were asked to score 1 to 5 points for each imaging result of each original image. High scores represent excellent imaging results [9]. The subjects were told that the excellent imaging results conveyed richer information to the observer. Whether it completely reconstructs the details of the original image is not important. In addition, the subjects were required to give a reasonable distribution of scores between 1 and 5 for each composition image result. This avoids uneven scores due to the different personal habits of the subjects.

Table 1 shows the score statistics of the feature imaging results in the four scene types. It is not difficult to see that different local features signify different local attributes of the original image. The visual system's sensitivity to it varies. The imaging results that highlight high-contrast areas have an advantage. This shows that the visual system prioritizes areas with strong contrast.

Single feature subjective scoring results statistics (mean ± standard deviation).

Feature | Contrast | Edge density | Directional difference | Symmetry |
---|---|---|---|---|

Human face | 2.10±0.12 | 3.11±0.61 | 2.37±0.01 | 2.47±0.17 |

Typical objects | 3.30±0.43 | 2.95±0.5 | 2.14±0.11 | 2.81±0.21 |

Indoor scene | 2.88±0.17 | 2.69±0.17 | 2.24±0.08 | 0.41±0.17 |

Outdoor scene | 3.07±0.17 | 2.24±0.11 | 2.19±0.08 | 2.88±0.19 |

The imaging results that highlight areas with high edge density followed closely behind. This verifies the important position of the edge in the understanding of visual information. The particular directional area and the symmetrical area are generally effective in the first three sets of scenes. Still, they are more prominent in outdoor scenes than the areas with higher edge density.

We take the imaging results reflecting the 4 local features as 4 totals. At the same time, we use the score of each imaging result as each overall sample to implement a one-way analysis of variance. The corresponding null hypothesis probability values are shown in Table 2. The null hypothesis in each set of scenarios at the significance level of 0.005 was rejected. We believe that different local salient features have obvious differences in the function of visual information transmission.

Single-feature imaging results, single-factor analysis of variance results.

Scene type | ||||
---|---|---|---|---|

human face | Typical objects | Indoor scene | Outdoor scene | |

Null hypothesis probability | 3.8×10^{−3} |
3.3×10^{−4} |
4.2×10^{−4} |
1.2×10^{−4} |

The above experimental results show that different local salient features affect imaging results under different scene types. In terms of visual information transmission, they have their unique attributes and are restricted by the type of scene. This proves the necessity of using several local feature detectors to extract salient features separately [10]. The score ratio of each feature after pixelation provides a basis for selecting the weight coefficient of the feature fusion step.

In this experiment, each original image first uses the grayscale variance as the uniformity criterion to implement pixelation to generate a test image. This can be used to compare the imaging results produced by the complete processing flow of the model. Then, after the complete processing flow of the model, the feature fusion step is implemented to produce two imaging results. The mean value is taken when the saliency of each feature is merged. We take the weighting coefficient _{k}_{k}

In this experiment, the original images’ selection, quantity, grouping method, and presentation interface are the same as those in the previous experiment. The subjects did not change either. The subjects did not obtain any data about the results of the previous experiment, nor did they know how the image to be tested was generated [11]. The subjects were asked to score the 3 imaging results of each original image. It must contain a score of 0, a score of 1, and a score of 2. A high score means that the imaging results convey richer information. Because the total score of each set of scene types is a fixed 36 points. This examines the proportion of each imaging result in the total score. The scoring results of faces, familiar objects, indoor scenes, and outdoor scenes are shown in Figure 3 (a) ~ (d) in turn. Among them, white represents the percentage of scores that are pixelated according to the grayscale variance criterion. Black indicates the average pixelized score ratio of the feature. Gray is the pixelized score ratio after feature weighting according to the previous experimental result. After the pixelization process is performed on the feature map obtained by the saliency weighted summation, the score ratio of the image is dominant.

The frequency of 2 points, 1 point, and 0 points obtained after the pixelization process is implemented according to the feature map obtained by the saliency weighted summation. The results are shown in Figure 4. The black bars, gray bars, and white bars represent the average times of 2, 1, and 0 points, respectively. It can be seen that the average number of 2 points is the most. This result indicates that the use of salient features as the criteria for the regional splitting process will provide the visual system with more subjectively richer visual information. The imaging results obtained using weighted feature summation to implement fusion are more successful than other imaging results. The above simulated subjective evaluation experiments have verified the rationality and effectiveness of the imaging model to a certain extent.

This article proposes a pixelated imaging model based on salient local features. We design a simulation evaluation experiment based on subjective evaluation scores to examine the performance of this model. This model can give the subjects preferentially presenting the characteristically significant areas. This allows the subjects to feel richer visual information subjectively. Therefore, this article has reference value for further research on artificial vision compensation imaging technology.

#### Single feature subjective scoring results statistics (mean ± standard deviation).

Feature | Contrast | Edge density | Directional difference | Symmetry |
---|---|---|---|---|

Human face | 2.10±0.12 | 3.11±0.61 | 2.37±0.01 | 2.47±0.17 |

Typical objects | 3.30±0.43 | 2.95±0.5 | 2.14±0.11 | 2.81±0.21 |

Indoor scene | 2.88±0.17 | 2.69±0.17 | 2.24±0.08 | 0.41±0.17 |

Outdoor scene | 3.07±0.17 | 2.24±0.11 | 2.19±0.08 | 2.88±0.19 |

#### Single-feature imaging results, single-factor analysis of variance results.

Scene type | ||||
---|---|---|---|---|

human face | Typical objects | Indoor scene | Outdoor scene | |

Null hypothesis probability | 3.8×10^{−3} |
3.3×10^{−4} |
4.2×10^{−4} |
1.2×10^{−4} |

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