Image color saturation difference testing method suitable for real-time streaming

Abstract

The invention discloses an image color saturation difference testing method suitable for real-time streaming. The method comprises the following steps: S1, preparing a plurality of pictures with rich colors; s2, uploading the picture to a streaming server side; s3, the pictures are played on a streaming server in a full-screen mode to the maximum extent; s4, connecting a server side by using a streaming client side; s5, performing screenshot storage on the picture displayed by the streaming client; s6, obtaining RGB values of the original image and the screenshot; s7, calculating the values of the color h, the saturation s and the lightness v of each pixel point of each original image and the corresponding screenshot; and comparing the color h, the saturation s and the brightness v of each pixel point of each original image and the corresponding screenshot to obtain a difference value result. According to the invention, the differences of the color h, the saturation degree s and the lightness v of each pixel point are compared, so that the differences of the pixel points before and after streaming are obtained; compared with human eye comparison, the dog can be more objective and accurate, and human eye errors are reduced.

Description

Technical Field

[0001] The present invention relates to the field of streaming technology, and in particular to a method for testing image color saturation differences applicable to real-time streaming. Background Art

[0002] The existing testing technology generally uses the naked eye for testers to view the stream and judge the color difference. For some images with low color saturation, the perception is not obvious, and it is impossible to accurately calculate whether the color after streaming is different from the original image. During the video streaming process, the difference in image color will cause users to make abnormal color judgments, and the color loss of the image affects the user experience. Therefore, a new streaming color measurement method is needed.

[0003] Therefore, the prior art has defects and needs to be improved. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a testing method for the color saturation difference of real-time streaming images, which can objectively evaluate the color and saturation of streaming images and avoid errors caused by the naked eye.

[0005] The technical solution of the present invention is as follows: a method for testing the color saturation difference of an image suitable for real-time streaming is provided, comprising the following steps.

[0006] S1: Prepare multiple colorful pictures.

[0007] S2: Upload the above picture to the streaming server.

[0008] S3: Maximize and play the above picture in full screen on the streaming server.

[0009] S4: Use the streaming client to connect to the server.

[0010] S5: Take a screenshot of the image displayed on the streaming client and save it.

[0011] S6: Obtain RGB values ​​of the original image and the screenshot.

[0012] S7: Calculate the color h, saturation s, and brightness v of each pixel of each original image and the corresponding screenshot. The calculation formula is as follows:

[0013]

[0014] v=max.

[0015] Among them, r is the value of R in the RGB value, b is the value of B in the RGB value, and g is the value of G in the RGB value; max and min are the maximum and minimum values ​​of r, g, and b respectively.

[0016] S8: Compare the color h, saturation s, and brightness v of each pixel point between each original image and the corresponding screenshot to obtain a difference value result.

[0017] The HSV color space is widely used in image processing. It is closer to people's perception of color than RGB. It expresses the hue, vividness and brightness of the color very intuitively, which is convenient for color comparison. The RGB color space is more oriented to industry, while HSV is more oriented to users. Therefore, using HSV for testing is more in line with user needs. By comparing the color h, saturation s, and brightness v of each pixel, the difference between the pixels before and after the streaming can be obtained; compared with the comparison by human eyes, it can be more objective and accurate, reducing the error of the human eye.

[0018] Furthermore, the step S7 also includes: averaging the color H, saturation S, and brightness V of all pixels of each original image, and the calculation results of each original image need to be distinguished; averaging the color H, saturation S, and brightness V of all pixels of each screenshot, and the calculation results of each screenshot need to be distinguished; the step S8 also includes: comparing the difference between the average values ​​of color H, saturation S, and brightness V of each original image and the corresponding screenshot, and obtaining the difference value result. By comparing the difference between the average values ​​of color H, saturation S, and brightness V of each original image and the corresponding screenshot, the overall color loss of the entire image can be intuitively seen.

[0019] Furthermore, colorful images should have the characteristics of sharp color contrast, strong color sense, and rich colors and layers.

[0020] Furthermore, in step S3, the image size and resolution should correspond to the display resolution of the streaming server, so that the image fills the entire screen without being stretched or having black edges.

[0021] Furthermore, in step S4, the streaming client uses a display device with the same resolution as the streaming server and plays the image of the streaming server in full screen.

[0022] Furthermore, in step S5, the streaming service end needs to play each picture in rotation, and the streaming client end captures a screenshot of each picture.

[0023] Furthermore, the method for testing the color saturation difference of images suitable for real-time streaming also includes: S9: outputting the difference value result.

[0024] By adopting the above scheme, the present invention provides a testing method for the color saturation difference of real-time streaming images. Compared with the tester's naked eye observation in conjunction with the screen display, the present invention can more accurately detect the color difference, obtain effective data samples, and determine the color loss of the image before and after streaming. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0026] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0027] See also Figure 1 The present invention provides a method for testing the color saturation difference of an image suitable for real-time streaming, comprising the following steps.

[0028] S1: Prepare multiple colorful pictures.

[0029] S2: Upload the above picture to the streaming server.

[0030] S3: Maximize and play the above picture in full screen on the streaming server.

[0031] S4: Use the streaming client to connect to the server.

[0032] S5: Take a screenshot of the image displayed on the streaming client and save it.

[0033] S6: Obtain RGB values ​​of the original image and the screenshot.

[0034] S7: Calculate the color h, saturation s, and brightness v of each pixel of each original image and the corresponding screenshot. The calculation formula is as follows:

[0035]

[0036] v=max.

[0037] Among them, r is the value of R in the RGB value, b is the value of B in the RGB value, and g is the value of G in the RGB value; max and min are the maximum and minimum values ​​of r, g, and b respectively.

[0038] S8: Compare the color h, saturation s, and brightness v of each pixel point between each original image and the corresponding screenshot to obtain a difference value result.

[0039] The HSV color space is widely used in image processing. It is closer to people's perception of color than RGB. It expresses the hue, vividness and brightness of the color very intuitively, which is convenient for color comparison. The RGB color space is more oriented to industry, while HSV is more oriented to users. Therefore, using HSV for testing is more in line with user needs. By comparing the color h, saturation s, and brightness v of each pixel, the difference between the pixels before and after the streaming can be obtained; compared with the comparison by human eyes, it can be more objective and accurate, reducing the error of the human eye.

[0040] In this embodiment, the step S7 also includes: averaging the color H, saturation S, and brightness V of all pixels of each original image, and the calculation results of each original image need to be distinguished; averaging the color H, saturation S, and brightness V of all pixels of each screenshot, and the calculation results of each screenshot need to be distinguished; the step S8 also includes: comparing the difference between the average values ​​of color H, saturation S, and brightness V of each original image and the corresponding screenshot, and obtaining the difference value result. By comparing the difference between the average values ​​of color H, saturation S, and brightness V of each original image and the corresponding screenshot, the overall color loss of the entire image can be intuitively seen.

[0041] In this embodiment, the colorful image should meet the characteristics of sharp color contrast, strong color sense, and rich colors and layers.

[0042] In this embodiment, in step S3, the image size and resolution should correspond to the display resolution of the streaming server, so that the image fills the entire screen without being stretched or having black edges.

[0043] In this embodiment, in step S4, the streaming client uses a display device with the same resolution as the streaming server and plays the image of the streaming server in full screen.

[0044] In this embodiment, in step S5, the streaming service end needs to play each picture in rotation, and the streaming client end captures a screenshot of each picture.

[0045] In this embodiment, the method for testing the color saturation difference of a real-time streaming image further includes: S9: outputting a difference value result.

[0046] In summary, the present invention provides a method for testing the color saturation difference of an image suitable for real-time streaming. Compared with the tester's naked eye observation in conjunction with the screen display, the present invention can more accurately detect the color difference, obtain valid data samples, and determine where the image color is lost before and after streaming.

[0047] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for testing the color saturation difference of a real-time streaming image, characterized in that: The following steps are involved: S1: Prepare multiple colorful pictures; S2: upload the above picture to the streaming server; S3: maximize the full screen playback of the above picture on the streaming server; S4: Use the streaming client to connect to the server; S5: taking a screenshot of the image displayed on the streaming client and saving it; S6: Obtain RGB values ​​of the original image and the screenshot; S7: Calculate the color h, saturation s, and brightness v of each pixel of each original image and the corresponding screenshot. The calculation formula is as follows: Among them, r is the value of R in RGB value, b is the value of B in RGB value, g is the value of G in RGB value; max and min are the maximum and minimum values ​​of r, g and b respectively; S8: Compare the color h, saturation s, and brightness v of each pixel point between each original image and the corresponding screenshot to obtain a difference value result.

2. The method for testing the color saturation difference of an image suitable for real-time streaming according to claim 1, characterized in that: The step S7 also includes: averaging the color H, saturation S, and brightness V of all pixels of each original image, and the calculation results of each original image need to be distinguished; averaging the color H, saturation S, and brightness V of all pixels of each screenshot, and the calculation results of each screenshot need to be distinguished; the step S8 also includes: comparing the difference in the average values ​​of color H, saturation S, and brightness V between each original image and the corresponding screenshot to obtain a difference value result.

3. The method for testing the color saturation difference of an image suitable for real-time streaming according to claim 1, characterized in that: Colorful images should have the characteristics of sharp color contrast, strong color sense, and rich colors and layers.

4. The method for testing the color saturation difference of an image suitable for real-time streaming according to claim 1, characterized in that: In step S3, the image size and resolution should correspond to the streaming server display resolution so that the image fills the entire screen without being stretched or having black edges.

5. The method for testing the color saturation difference of an image suitable for real-time streaming according to claim 1, characterized in that: In step S4, the streaming client uses a display device with the same resolution as the streaming server and plays the image of the streaming server in full screen.

6. The method for testing the color saturation difference of an image suitable for real-time streaming according to claim 1, characterized in that: In step S5, the streaming service needs to play each picture in turn, and the streaming client takes a screenshot of each picture.

7. The method for testing the color saturation difference of an image suitable for real-time streaming according to claim 1, characterized in that: Also includes: S9: Output the difference value result.

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