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CN-116152081-B - Brightness correction method, brightness correction device, electronic equipment and computer readable storage medium

CN116152081BCN 116152081 BCN116152081 BCN 116152081BCN-116152081-B

Abstract

The embodiment of the application provides a brightness correction method, a brightness correction device, electronic equipment and a computer readable storage medium, and relates to the technical field of computers. The method comprises the steps of determining second brightness correction parameters of a predicted area of a display interface through first brightness correction parameters and a preset parameter prediction model, and then carrying out brightness correction processing on the predicted area based on the second brightness correction parameters. The parameter prediction model is obtained based on model training of sample data, the sample data comprises a first sample correction parameter of a sample reference area and a second sample correction parameter of a sample prediction area, namely, the parameter prediction model acquires the relation between the first sample correction parameter and the second sample correction parameter in the training process, so that the accuracy of the second brightness correction parameter obtained through prediction is higher, and when brightness correction processing is carried out on the prediction area based on the second brightness correction parameter, the correction time can be shortened, and the correction processing efficiency is improved.

Inventors

  • XU CHAOFAN

Assignees

  • 京东方科技集团股份有限公司
  • 成都京东方光电科技有限公司

Dates

Publication Date
20260512
Application Date
20221021

Claims (11)

  1. 1. A brightness correction method, comprising: Acquiring a first brightness correction parameter of a reference area of a display interface of electronic equipment; Determining a second brightness correction parameter of a predicted area of the display interface based on the first brightness correction parameter and a preset parameter prediction model; The reference area is a main display area, and the main display area is a display area outside the prediction area in a display interface; The parameter prediction model is obtained based on model training of sample data, wherein the sample data comprises a first sample correction parameter of a sample reference area and a second sample correction parameter of a sample prediction area; and performing brightness correction processing on the prediction area based on the second brightness correction parameter.
  2. 2. The method for luminance correction according to claim 1, wherein, The obtaining the first brightness correction parameter of the reference area of the display interface of the electronic device includes: acquiring a third brightness correction parameter of a reference area of the display interface under a preset brightness level and/or a preset gray level; and preprocessing the third brightness correction parameter to obtain the first brightness correction parameter.
  3. 3. The method for luminance correction according to claim 2, wherein, The obtaining the third brightness correction parameter of the reference area of the display interface under the preset brightness level and/or the preset gray level includes: And acquiring the brightness correction parameters of the target color channels of the reference area of the display interface as the third brightness correction parameters under the preset brightness level and/or the preset gray level.
  4. 4. A brightness correction method according to claim 3 characterized in that, in the case where said third brightness correction parameter comprises a brightness correction parameter of a target color channel of a reference area of said display interface at a preset brightness level and at a preset gray level, The preprocessing the third brightness correction parameter to obtain the first brightness correction parameter includes: And carrying out normalization processing and patterning processing on the third brightness correction parameters to obtain brightness image parameters corresponding to the third brightness correction parameters, wherein the first brightness correction parameters comprise the brightness image parameters.
  5. 5. The method of claim 1, wherein prior to obtaining the first luminance correction parameter for the reference area of the display interface of the electronic device, the method further comprises: Obtaining a training sample; Inputting the first sample brightness correction parameters of the training samples into an initial model to obtain a prediction result corresponding to each training sample, wherein the prediction result comprises the prediction brightness correction parameters of the sample prediction area; determining a training loss value according to the predicted brightness correction parameter and the second sample correction parameter; and based on the training loss value, repeating training on the initial model until the parameter prediction model meeting the training ending condition is obtained.
  6. 6. The luminance correction method according to claim 1, wherein the model structure of the parametric prediction model includes at least one of: at least two convolution layers; pooling layers; An array flattening layer; and (5) a full connection layer.
  7. 7. The method for luminance correction according to claim 1, wherein, The performing brightness correction processing on the prediction area based on the second brightness correction parameter includes: determining the second brightness correction parameter as an initial correction parameter for brightness correction processing of the prediction area; And carrying out brightness correction processing on the prediction area based on the initial correction parameters so as to determine target correction parameters of the prediction area.
  8. 8. The brightness correction method according to any one of claims 3 to 4, wherein the target color channel includes a first target color channel, a second target color channel, and a third target color channel; the brightness correction process includes gamma correction.
  9. 9. A brightness correction device, comprising: The acquisition module is used for acquiring a first brightness correction parameter of a reference area of a display interface of the electronic equipment; The prediction module is used for determining a second brightness correction parameter of a prediction area of the display interface based on the first brightness correction parameter and a preset parameter prediction model; The reference area is a main display area, and the main display area is a display area outside the prediction area in a display interface; The parameter prediction model is obtained based on model training of sample data, wherein the sample data comprises a first sample correction parameter of a sample reference area and a second sample correction parameter of a sample prediction area; and the correction module is used for carrying out brightness correction processing on the prediction area based on the second brightness correction parameter.
  10. 10. An electronic device, the electronic device comprising: one or more processors; a memory; One or more applications stored in the memory and configured to be executed by the one or more processors, the one or more applications configured to perform the luminance correction method according to any one of claims 1 to 8.
  11. 11. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the luminance correction method according to any one of claims 1 to 8.

Description

Brightness correction method, brightness correction device, electronic equipment and computer readable storage medium Technical Field The present application relates to the field of computer technology, and in particular, to a brightness correction method, a brightness correction device, an electronic device, and a computer readable storage medium. Background In an image display scene, the perception of the brightness of the display interface by the human eye is nonlinear, which can be reflected, for example, in a dark environment to a higher degree than in a bright environment. To accommodate the non-linearity of the human eye's perception of brightness, it is often necessary to correct the brightness of the display interface of the electronic device, for example, the process of correcting the brightness may include adjusting the gray-to-brightness relationship of the display interface to be non-linear. In general, when brightness correction is performed on a display interface, an initial brightness correction parameter is generally referred to, so that the accuracy of the initial brightness parameter affects the final brightness correction result. Disclosure of Invention The present application aims to solve at least one of the above technical drawbacks, and particularly to solve the technical drawbacks of a lower accuracy of an initial luminance correction parameter of luminance correction, so that a longer time of luminance correction and a lower luminance correction efficiency. According to an aspect of the present application, there is provided a brightness correction method, the method comprising: Acquiring a first brightness correction parameter of a reference area of a display interface of electronic equipment; Determining a second brightness correction parameter of a predicted area of the display interface based on the first brightness correction parameter and a preset parameter prediction model; The reference area is a main display area, and the main display area is a display area outside the prediction area in a display interface; The parameter prediction model is obtained based on model training of sample data, wherein the sample data comprises a first sample correction parameter of a sample reference area and a second sample correction parameter of a sample prediction area; and performing brightness correction processing on the prediction area based on the second brightness correction parameter. Optionally, the acquiring the first brightness correction parameter of the reference area of the display interface of the electronic device includes: acquiring a third brightness correction parameter of a reference area of the display interface under a preset brightness level and/or a preset gray level; and preprocessing the third brightness correction parameter to obtain the first brightness correction parameter. Optionally, the obtaining the third brightness correction parameter of the reference area of the display interface at the preset brightness level and/or the preset gray level includes: And acquiring the brightness correction parameters of the target color channels of the reference area of the display interface as the third brightness correction parameters under the preset brightness level and/or the preset gray level. Optionally, in the case that the third luminance correction parameter includes a luminance correction parameter of a target color channel of the reference area of the display interface at a preset luminance level and at a preset gray level, The preprocessing the third brightness correction parameter to obtain the first brightness correction parameter includes: And carrying out normalization processing and patterning processing on the third brightness correction parameters to obtain brightness image parameters corresponding to the third brightness correction parameters, wherein the first brightness correction parameters comprise the brightness image parameters. Optionally, before the obtaining the first brightness correction parameter of the reference area of the display interface of the electronic device, the method further includes: Obtaining a training sample; Inputting the first sample brightness correction parameters of the training samples into an initial model to obtain a prediction result corresponding to each training sample, wherein the prediction result comprises the prediction brightness correction parameters of the sample prediction area; determining a training loss value according to the predicted brightness correction parameter and the second sample correction parameter; and based on the training loss value, repeating training on the initial model until the parameter prediction model meeting the training ending condition is obtained. Optionally, the model structure of the parameter prediction model includes at least one of: at least two convolution layers; pooling layers; An array flattening layer; and (5) a full connection layer. Optionally, the performing brightn