Method, device and electronic device for determining human eye perception threshold

Abstract

The present invention provides a method, device, and electronic device for determining a human eye perception threshold. The method comprises obtaining a first image parameter of a target image region, wherein the first image parameter includes one or more of a brightness parameter, a texture parameter, and a color parameter. Based on a predetermined relationship between the image parameter and the perception threshold, a first perception threshold corresponding to the first image parameter is determined. The relationship between the image parameter and the perception threshold is determined based on image quality evaluation parameter values ​​for images under various noise conditions at various parameter values ​​of the image parameter. The perception threshold of the target image region is determined based on the first perception threshold corresponding to the first image parameter. This method utilizes the image quality evaluation parameter value to determine the perception threshold of the target image region. Compared to methods that measure the perception threshold through human visual observation, this method can more conveniently and quickly determine the perception threshold of the image region, thereby improving the efficiency of determining the human eye perception threshold.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and in particular to a method, device and electronic equipment for determining a human eye perception threshold. Background Art

[0002] JND (Just Noticeable Distortion) usually indicates the maximum image distortion value that cannot be perceived by the human eye, that is, the human eye perception threshold, which reflects the human eye's tolerance to image changes. The traditional JND model obtains the human eye perception threshold by measuring the masking effect of different background brightness and textures. Specifically, part of the perception threshold can be obtained by calculation, but the other part of the perception threshold needs to be obtained through human eye observation. In related technologies, the JND model can be applied to video coding. For areas with a large JND threshold, they can be compressed more due to low sensitivity. For areas with a smaller JND threshold, they can be compressed less. Ultimately, the bit rate can be saved without affecting the subjective visual experience. However, when it is necessary to count the changes in multiple image features, or the amount of image data is large, this method of measuring the perception threshold through human eye observation is time-consuming and labor-intensive. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a method, device and electronic device for determining the human eye perception threshold, which can conveniently and quickly determine the perception threshold of an image area and improve the efficiency of determining the human eye perception threshold.

[0004] In a first aspect, an embodiment of the present invention provides a method for determining a human eye perception threshold, comprising: obtaining a first image parameter of a target image area; wherein the first image parameter includes one or more of a brightness parameter, a texture parameter, and a color parameter; determining a first perception threshold corresponding to the first image parameter based on a predetermined relationship between the image parameter and the perception threshold; wherein the relationship between the image parameter and the perception threshold is determined based on an image quality evaluation parameter value under multiple parameter values ​​of the image parameter and multiple noises; and determining the perception threshold of the target image area based on the first perception threshold corresponding to the first image parameter.

[0005] Furthermore, the relationship between the image parameters and the perception threshold is determined specifically in the following manner: for each parameter value of the image parameter, an original image with the parameter value is obtained; for each original image, random noise of multiple specified amplitudes is added to the original image to obtain a noise image corresponding to each specified amplitude; based on the image quality evaluation parameter value of the noise image and the image quality evaluation parameter value of the original image, the relationship between the image parameter and the perception threshold is determined.

[0006] Furthermore, the step of determining the relationship between image parameters and perception thresholds based on the image quality evaluation parameter values ​​of the noise image and the image quality evaluation parameter values ​​of the original image includes: determining a first image quality evaluation parameter value of the original image and second image quality evaluation parameter values ​​of multiple noise images corresponding to the original image through an image quality evaluation model; determining a target image quality evaluation parameter value from the first image quality evaluation parameter value and the second image quality evaluation parameter value; and determining the relationship between image parameters and perception thresholds based on the target image quality evaluation parameter value.

[0007] Furthermore, the step of determining the target image quality evaluation parameter value from the first image quality evaluation parameter value and the second image quality evaluation parameter value includes: obtaining an intermediate image quality evaluation parameter value greater than or equal to the first image quality evaluation parameter value from the second image quality evaluation parameter value; and determining the maximum value among the intermediate image quality evaluation parameter values ​​as the target image quality evaluation parameter value.

[0008] Furthermore, based on the target image quality evaluation parameter value, the step of determining the relationship between the image parameter and the perception threshold includes: obtaining a target noise image corresponding to the target image quality evaluation parameter value; determining the specified amplitude corresponding to the target noise image as the perception threshold corresponding to the parameter value; and determining the correspondence between each parameter value of the image parameter and the perception threshold corresponding to the parameter value as the relationship between the image parameter and the perception threshold.

[0009] Furthermore, the image parameters include brightness parameters, texture parameters and color parameters; for each parameter value of the image parameters, the step of obtaining an original image with the parameter value includes: for each grayscale value of the brightness parameter, obtaining an original grayscale image with a grayscale value; wherein the grayscale value of each pixel in the original grayscale image is the same; for each texture value of the texture parameter, obtaining an original texture image with a texture value; wherein the texture value includes an average gradient value; for each color value of the color parameter, obtaining an original color image with a color value; wherein the color value includes an RGB value.

[0010] Furthermore, based on the relationship between the predetermined image parameters and the perception threshold, the step of determining the first perception threshold corresponding to the first image parameter includes: obtaining the perception threshold corresponding to the first parameter value from the relationship between the predetermined image parameters and the perception threshold according to the first parameter value of the image parameter included in the first image parameter; and determining the perception threshold corresponding to the first parameter value as the first perception threshold.

[0011] Furthermore, the first image parameters include multiple; the step of determining the perception threshold of the target image area according to the first perception threshold corresponding to the first image parameters includes: determining the maximum perception threshold among the first perception thresholds corresponding to each first image parameter as the perception threshold of the target image area.

[0012] Furthermore, after the step of determining the perception threshold of the target image area according to the first perception threshold corresponding to the first image parameter, the method further includes: encoding the target image area based on the perception threshold of the target image area.

[0013] In a second aspect, an embodiment of the present invention provides a device for determining a human eye perception threshold, comprising: an acquisition module for acquiring a first image parameter of a target image area; wherein the first image parameter includes one or more of a brightness parameter, a texture parameter, and a color parameter; a first determination module for determining a first perception threshold corresponding to the first image parameter based on a predetermined relationship between the image parameter and the perception threshold; wherein the relationship between the image parameter and the perception threshold is determined based on an image quality evaluation parameter value under multiple parameter values ​​of the image parameter and multiple noises; a second determination module for determining the perception threshold of the target image area based on the first perception threshold corresponding to the first image parameter.

[0014] In a third aspect, an embodiment of the present invention provides an electronic device comprising a processor and a memory, wherein the memory stores machine-executable instructions that can be executed by the processor, and the processor executes the machine-executable instructions to implement the method for determining the human eye perception threshold of any one of the first aspects.

[0015] In a fourth aspect, an embodiment of the present invention provides a machine-readable storage medium, which stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to implement the method for determining the human eye perception threshold of any one of the first aspects.

[0016] The embodiments of the present invention bring the following beneficial effects:

[0017] Embodiments of the present invention provide a method, apparatus, and electronic device for determining a human eye perception threshold. The method comprises obtaining a first image parameter of a target image region, wherein the first image parameter includes one or more of a brightness parameter, a texture parameter, and a color parameter. Based on a predetermined relationship between the image parameter and the perception threshold, a first perception threshold corresponding to the first image parameter is determined. The relationship between the image parameter and the perception threshold is determined based on image quality evaluation parameter values ​​for images under various noise conditions at various parameter values ​​of the image parameter. The perception threshold of the target image region is determined based on the first perception threshold corresponding to the first image parameter. This method utilizes the image quality evaluation parameter value to determine the perception threshold of the target image region. Compared to methods that measure the perception threshold through human visual observation, this method can quickly and easily determine the perception threshold of the image region, improving the efficiency of determining the human eye perception threshold.

[0018] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or understood by practicing the present invention. The purposes and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 A flowchart of a method for determining a human eye perception threshold provided by an embodiment of the present invention;

[0022] Figure 2 A flowchart of another method for determining a human eye perception threshold provided by an embodiment of the present invention;

[0023] Figure 3 A flowchart of another method for determining a human eye perception threshold provided by an embodiment of the present invention;

[0024] Figure 4 A schematic diagram of an image noise addition process provided by an embodiment of the present invention;

[0025] Figure 5 A schematic structural diagram of a device for determining a human eye perception threshold provided by an embodiment of the present invention;

[0026] Figure 6 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0028] Due to the sensitivity and masking characteristics of human beings in pixel space and time, the human eye cannot perceive changes below the JND threshold; JND (Just Noticeable Distortion) usually represents the maximum image distortion value that the human eye cannot perceive, that is, the human eye perception threshold, which reflects the human eye's tolerance to image changes. The traditional JND model obtains the human eye perception threshold by measuring the masking effect of different background brightness and textures. Specifically, part of the perception threshold can be obtained by calculation, but the other part of the perception threshold needs to be obtained through human eye observation. In related technologies, the JND model can be applied to video coding. For areas with a large JND threshold, due to low sensitivity, they can be compressed more. For areas with a smaller JND threshold, they can be compressed less. Ultimately, the bit rate can be saved without affecting the subjective visual experience. However, when it is necessary to count the changes in multiple image features or the amount of image data is large, this method of measuring the perception threshold through human eye observation is time-consuming and labor-intensive. Based on this, the embodiments of the present invention provide a method, device and electronic device for determining the human eye perception threshold. This technology can be applied to mobile phones, computers, cameras and other devices, especially to devices with video (or image) encoding functions.

[0029] To facilitate understanding of this embodiment, a method for determining a human eye perception threshold disclosed in an embodiment of the present invention is first introduced in detail. Figure 1 As shown, the method includes the following steps:

[0030] Step S102, obtaining first image parameters of the target image area; wherein the first image parameters include one or more of brightness parameters, texture parameters, and color parameters;

[0031] The above-mentioned target image area generally refers to an image or video frame to be encoded, wherein the target image area can be a pixel point; the above-mentioned target image area can be a single-channel image or a multi-channel image. If the target image area is a single-channel image, the first image parameter includes one of a brightness parameter, a texture parameter and a color parameter; if the target image area is a multi-channel image, the first image parameter includes multiple of a brightness parameter, a texture parameter and a color parameter.

[0032] Specifically, a grayscale image of the target image region may be extracted from the region, and the parameter value of the brightness parameter may be obtained from the grayscale image; texture features of the region may be extracted from the region, and the parameter value of the texture parameter may be obtained from the texture features; color features of the region may be extracted from the region, and the parameter value of the color parameter may be obtained from the color features;

[0033] Step S104: determining a first perception threshold corresponding to the first image parameter based on a predetermined relationship between the image parameter and the perception threshold; wherein the relationship between the image parameter and the perception threshold is determined based on image quality evaluation parameter values ​​of the image under various parameter values ​​of the image parameter and various noise conditions;

[0034] The predetermined relationship between the image parameter and the perception threshold may include relationships between multiple image parameters and the perception threshold. Since the first image parameter includes one or more of a brightness parameter, a texture parameter, and a color parameter, a first perception threshold corresponding to each first image parameter can be determined based on the predetermined relationship between the image parameter and the perception threshold. The type and amount of noise can be set according to actual needs, and may specifically include noise intensity and type. The image quality assessment parameter value may be an image quality score.

[0035] Adding multiple noises to images under multiple parameter values ​​of image parameters, specifically, randomly adding multiple noises to each pixel in the image, to obtain images under multiple noises; determining the relationship between the image parameter and the perception threshold based on the image quality evaluation parameter values ​​under the multiple noises; specifically, determining the perception threshold corresponding to the multiple parameter values ​​of the image parameter based on the magnitude of the image quality evaluation parameter value, and then determining the relationship between the image parameter and the perception threshold. For example, if the above-mentioned image parameter includes a brightness parameter, the multiple parameter values ​​of the image parameter can be grayscale values ​​within a range of 0 to 255 with an interval of 1 or 2; adding multiple noises to each image with the grayscale value to obtain images under the multiple noises; determining the relationship between each parameter value of the image parameter and the perception threshold based on the magnitude of the image quality evaluation parameter value under the multiple noises.

[0036] Step S106: determining a perception threshold of the target image area according to the first perception threshold corresponding to the first image parameter.

[0037] Since the above-mentioned first image parameters include one or more of brightness parameters, texture parameters, and color parameters, if the first image parameters include one of the brightness parameters, texture parameters, and color parameters, the first perception threshold corresponding to the first image parameter can be directly determined as the perception threshold of the target image area. If the first image parameters include multiple of the brightness parameters, texture parameters, and color parameters, the maximum or minimum value of the first perception thresholds corresponding to each first image parameter can be determined as the perception threshold of the target image area; the average value of the first perception thresholds corresponding to each first image parameter can also be determined as the perception threshold of the target image area; and the sum of the first perception thresholds corresponding to each first image parameter can also be determined as the perception threshold of the target image area.

[0038] An embodiment of the present invention provides a method for determining a human eye perception threshold, comprising obtaining a first image parameter of a target image region; wherein the first image parameter includes one or more of a brightness parameter, a texture parameter, and a color parameter; determining a first perception threshold corresponding to the first image parameter based on a predetermined relationship between the image parameter and the perception threshold; wherein the relationship between the image parameter and the perception threshold is determined based on image quality evaluation parameter values ​​for images under various noise conditions at various parameter values ​​of the image parameter; and determining the perception threshold of the target image region based on the first perception threshold corresponding to the first image parameter. In this method, the perception threshold of the target image region is determined using the image quality evaluation parameter value. Compared to methods that measure the perception threshold through human visual observation, this method can conveniently and quickly determine the perception threshold of the image region, thereby improving the efficiency of determining the human eye perception threshold.

[0039] This embodiment also provides another method for determining the human eye perception threshold. This method is implemented on the basis of the above embodiment. This embodiment mainly describes the implementation of the relationship between the predetermined image parameters and the perception threshold, such as Figure 2 As shown, the method includes the following steps:

[0040] Step S202: for each parameter value of the image parameter, obtaining an original image with the parameter value;

[0041] The image parameters include brightness, texture, and color parameters; each image parameter has multiple parameter values. The original image can be obtained based on the type of image parameter. For each type of image parameter value, an original image with that parameter value is obtained from a pre-set set of original images. For example, if the image parameter is brightness, an original image with a grayscale value of 128 can be obtained. The original image can be a grayscale image of a preset size, such as a 32*32 pixel size with a grayscale value of 128 for each pixel.

[0042] It should be noted that different types of original images can be pre-set. For example, an original grayscale image can be a grayscale image with grayscale values ​​ranging from 0 to 255, and the grayscale value of each pixel in each original grayscale image is the same. For another example, an original texture image can be an image in an image dataset, such as the open source DescribableTextures Dataset (DTD) texture dataset. The average gradient value of each image in the texture dataset can be determined as the texture parameter value of the original texture image. For another example, an original color image can be a color image with GRB values ​​ranging from 0 to 255, and the color value of each pixel in each original color image is the same. In reality, it is possible to obtain an original image with parameter values ​​based on each parameter value of the image parameter.

[0043] Step S204: for each original image, adding random noise of multiple specified amplitudes to the original image to obtain a noise image corresponding to each specified amplitude;

[0044] The above-mentioned specified amplitude can be set according to actual needs. For example, the specified amplitude can be a value with an amplitude interval of 1 and a range of 0 to 25. For each original image, random noise of multiple specified amplitudes is randomly added to each pixel in the original image to obtain a noise image corresponding to each specified amplitude; the random noise can be additive noise or subtractive noise; for example, for each original image, 25 types of random noise of specified amplitudes are randomly added to each pixel in the original image to obtain 25 noise images corresponding to the 25 specified amplitudes;

[0045] Specifically, it can be obtained by the following formula: I(x,y)=I(x,y)+α*S rand *R; where I(x,y) represents the pixel value of each pixel in the original image; α represents the amplitude value of the specified amplitude; S rand Indicates the addition of additive noise or subtractive noise. If additive noise S is added rand The value is -1, if subtractive noise S is added randThe value is 1; R represents whether noise is added randomly or not. If noise is added randomly, R takes the value of 1; if noise is not added randomly, R takes the value of 0. Figure 3 The schematic diagram of image noise processing shown in the figure takes the original image with a grayscale value of 128 and a pixel size of 32*32 as an example, randomly adds random noise with a fixed amplitude of 10, and obtains a noise image corresponding to the specified amplitude of 10.

[0046] Step S206 : determining the relationship between the image parameter and the perception threshold based on the image quality evaluation parameter value of the noise image and the image quality evaluation parameter value of the original image.

[0047] Specifically, the image quality evaluation parameter values ​​of the noise image and the original image can be determined by means of an image quality evaluation model, etc.; based on the numerical values ​​of the image quality evaluation parameter values ​​of the acoustic image and the image quality evaluation parameter values ​​of the original image, it can be determined whether the image quality of the original image has changed after the noise processing has been performed; based on the noise image whose image quality evaluation parameter value has not changed or has increased, and the numerical value of the specified amplitude added to the noise image, the perception threshold corresponding to the parameter value of the original image is determined, and then based on the perception threshold corresponding to the parameter value of each original image, the relationship between the image parameter and the perception threshold is determined.

[0048] In the above method, for each parameter value of an image parameter, an original image with that parameter value is obtained. For each original image, random noise of various specified amplitudes is added to the original image to obtain a noise image corresponding to each specified amplitude. Based on the image quality evaluation parameter values ​​of the noise image and the image quality evaluation parameter values ​​of the original image, the relationship between the image parameter and the perception threshold is determined. In this method, the perception threshold of the target image area is determined using the image quality evaluation parameter values. Compared to methods that measure the perception threshold through human observation, this method improves the efficiency of determining the human perception threshold, thereby reducing the bit rate during the image encoding process.

[0049] This embodiment also provides another method for determining the human eye perception threshold. This method is implemented on the basis of the above embodiment. This embodiment focuses on describing the specific implementation method of the step of obtaining the original image with the parameter value for each parameter value of the image parameter (implemented by steps S402-S406), and the specific implementation method of the step of determining the relationship between the image parameter and the perception threshold based on the image quality evaluation parameter value of the noise image and the image quality evaluation parameter value of the original image (implemented by steps S410-S414). The above image parameters include brightness parameters, texture parameters, and color parameters; Figure 4 As shown, the method includes the following steps:

[0050] Step S402: for each grayscale value of the brightness parameter, obtaining an original grayscale image having a grayscale value; wherein the grayscale value of each pixel in the original grayscale image is the same;

[0051] Step S404: for each texture value of the texture parameter, obtaining an original texture image having the texture value; wherein the texture value includes an average gradient value;

[0052] Step S406, for each color value of the color parameter, obtaining an original color image having a color value; wherein the color value includes an RGB value;

[0053] For each parameter value of the three image parameters, an original image with the parameter value can be pre-set; including an original grayscale image with a pixel size of 32*32, the grayscale value of each pixel being the same, with an interval of 1 or 2, and a grayscale value range of 0 to 255; based on the original grayscale image and the corresponding grayscale value, for each grayscale value of the brightness parameter, an original grayscale image with a grayscale value is obtained. An open source texture dataset is also included, and the average gradient value of each original texture image in the texture dataset is calculated, and the average gradient value represents the texture value of the corresponding original texture image; based on the original texture image and the corresponding texture value, for each texture value of the texture parameter, an original texture image with a texture value is obtained. Also included is an original color image with a preset pixel size, the color value (GRB value) of each pixel being the same, with an interval of 1, and a color value range of 0 to 255; based on the original color image and the corresponding GRB value, for each GRB value of the color parameter, an original color image with the GRB value is obtained.

[0054] It should be noted that the above-mentioned image parameters may also include clarity parameters, shape parameters, space parameters, etc.

[0055] Step S408: For each original image, add random noise of multiple specified amplitudes to the original image to obtain a noise image corresponding to each specified amplitude;

[0056] Step S410, determining a first image quality evaluation parameter value of the original image and second image quality evaluation parameter values ​​of multiple noise images corresponding to the original image through an image quality evaluation model;

[0057] The above-mentioned image quality evaluation model can be a natural image quality evaluation model (Natural Image Quality Evaluator, NIQE) or the like; specifically, the original image can be input into the image quality evaluation model to obtain a first image quality evaluation parameter value, and at the same time, multiple noise images corresponding to the original image can be input into the image quality evaluation model to obtain a second image quality evaluation parameter value; the number of the second image quality evaluation parameter values ​​is the same as the number of noise images corresponding to the original image, that is, the second image quality evaluation parameter value corresponding to each noise image is obtained.

[0058] Step S412, determining a target image quality evaluation parameter value from the first image quality evaluation parameter value and the second image quality evaluation parameter value;

[0059] Specifically, the image quality evaluation parameter value with the largest value can be determined from the second image quality evaluation parameter values ​​based on the numerical values ​​of the first image quality evaluation parameter value and the second image quality evaluation parameter value; or the image quality evaluation parameter value with the largest value can be directly determined from the first image quality evaluation parameter value and the second image quality evaluation parameter value.

[0060] One possible implementation:

[0061] (1) obtaining an intermediate image quality evaluation parameter value that is greater than or equal to the first image quality evaluation parameter value from the second image quality evaluation parameter values;

[0062] (2) The maximum value among the intermediate image quality evaluation parameter values ​​is determined as the target image quality evaluation parameter value.

[0063] Since noise of different specified amplitudes is added to the original image, the second image quality evaluation parameter value of the noise image will usually decrease or remain unchanged; specifically, an intermediate image quality evaluation parameter value greater than or equal to the first image quality evaluation parameter value can be obtained from the second image quality evaluation parameter value; the intermediate image quality value can be one or more; when the intermediate image quality evaluation parameter value is one, the intermediate image quality evaluation parameter value can be directly determined as the target image quality evaluation parameter value. When the intermediate image quality evaluation parameter value is multiple, the maximum value among the multiple intermediate image quality evaluation parameter values ​​can be determined as the target image quality evaluation parameter value. If there are multiple intermediate image quality evaluation parameter values ​​and the multiple intermediate image quality evaluation parameter values ​​are the same, the noise images corresponding to the multiple intermediate image quality evaluation parameter values ​​can be obtained; the maximum amplitude value in the specified amplitude corresponding to the noise image is determined, and the image quality evaluation parameter value corresponding to the noise image corresponding to the maximum amplitude value is determined as the target image quality evaluation parameter value.

[0064] Step S414: determining the relationship between the image parameter and the perception threshold based on the target image quality evaluation parameter value.

[0065] Specifically, the specified amplitude corresponding to the noise image can be obtained based on the noise image corresponding to the target image quality evaluation parameter value; based on the specified amplitude, the perception threshold corresponding to the parameter value of the image parameter is determined, and then the relationship between the image parameter and the perception threshold is determined.

[0066] One possible implementation:

[0067] (1) obtaining a target noise image corresponding to a target image quality evaluation parameter value; determining a specified amplitude corresponding to the target noise image as a perception threshold corresponding to the parameter value;

[0068] (2) The corresponding relationship between each parameter value of the image parameter and the perception threshold corresponding to the parameter value is determined as the relationship between the image parameter and the perception threshold.

[0069] Specifically, when the image parameter is a brightness parameter, a target noise image corresponding to the target image quality evaluation parameter value can be obtained; the specified amplitude corresponding to the target noise image is determined as the perception threshold corresponding to the grayscale value; 0-255 grayscale values ​​and the perception threshold corresponding to each grayscale value can be obtained; 0-255 grayscale values ​​are used as the horizontal coordinates, and the perception threshold corresponding to each grayscale value is used as the vertical coordinate. Through curve fitting, the correspondence between the grayscale parameter and the perception threshold is obtained, that is, each grayscale value corresponds to a perception threshold, and the human eye cannot perceive pixel changes below the perception threshold.

[0070] When the image parameter is a texture parameter, a target noise image corresponding to the target image quality evaluation parameter value can be obtained; the specified amplitude corresponding to the target noise image is determined as the perception threshold corresponding to the texture value (average gradient value); multiple average gradient values ​​and the perception threshold corresponding to each average gradient value can be obtained; the numerical values ​​of the multiple average gradient values ​​are used as the horizontal coordinates, and the perception threshold corresponding to each average gradient value is used as the vertical coordinate. Through curve fitting, the corresponding relationship between the texture parameter and the perception threshold is obtained, that is, each average gradient value corresponds to a perception threshold, and the human eye cannot perceive pixel changes below the perception threshold.

[0071] When the image parameter is a color parameter, a target noise image corresponding to the target image quality evaluation parameter value can be obtained; the specified amplitude corresponding to the target noise image is determined as the perception threshold corresponding to the color value (GRB value); multiple GRB values ​​(usually GRB values ​​in the range of 0-255) and the perception threshold corresponding to each GRB value can be obtained; the GRB value in the range of 0-255 is used as the horizontal axis, and the perception threshold corresponding to each GRB value is used as the vertical axis. Through curve fitting, the correspondence between the color parameter and the perception threshold is obtained, that is, each GRB value corresponds to a perception threshold, and the human eye cannot perceive pixel changes below this perception threshold.

[0072] The above method specifically describes the process of determining the relationship between image parameters and perceptual thresholds. Using an image quality assessment model, a first image quality assessment parameter value for the original image and second image quality assessment parameter values ​​for various noise images corresponding to the original image are determined. A target image quality assessment parameter value is determined from the first and second image quality assessment parameter values. Based on the target image quality assessment parameter value, the relationship between the image parameters and the perceptual threshold is determined. This method determines the perceptual threshold of the target image region using the image quality assessment parameter value and a specified amplitude. Compared to methods that measure the perceptual threshold through human visual observation, this method improves the efficiency of determining the human perceptual threshold, thereby reducing the bit rate during image encoding.

[0073] This embodiment mainly describes the steps of determining a first perception threshold corresponding to a first image parameter based on a predetermined relationship between the image parameter and the perception threshold, including:

[0074] According to a first parameter value of an image parameter included in the first image parameter, a perception threshold corresponding to the first parameter value is obtained from a predetermined relationship between the image parameter and the perception threshold; and the perception threshold corresponding to the first parameter value is determined as the first perception threshold.

[0075] When performing image or video encoding, it is first necessary to obtain the first image parameters of the target image area, which may include one or more image parameters. For example, if the first image parameters include a brightness parameter with a grayscale value of 130, the perception threshold corresponding to a grayscale value of 130 can be determined from the predetermined relationship between the brightness parameter and the perception threshold; this perception threshold is determined as the first perception threshold. If the first image parameters also include a texture parameter with an average gradient value of 70, the perception threshold corresponding to an average gradient value of 70 can also be obtained; this perception threshold is determined as the first perception threshold. If the first image parameters also include a color parameter with a GRB value of 100, the perception threshold corresponding to a GRB value of 100 can also be obtained; this perception threshold is determined as the first perception threshold.

[0076] The first image parameter includes a plurality of parameters; and the step of determining the perception threshold of the target image area according to the first perception threshold corresponding to the first image parameter includes:

[0077] The maximum perception threshold among the first perception thresholds corresponding to each first image parameter is determined as the perception threshold of the target image area.

[0078] For example, the first perception threshold corresponding to the brightness parameter, the first perception threshold corresponding to the texture parameter, and the first perception threshold corresponding to the color parameter; and the maximum perception threshold among the three first perception thresholds are determined as the perception threshold of the target image area.

[0079] Furthermore, after the step of determining the perception threshold of the target image area according to the first perception threshold corresponding to the first image parameter, the method further includes: encoding the target image area based on the perception threshold of the target image area.

[0080] Specifically, based on the perception threshold of the target image area, the perception threshold of the target image area can be replaced with the JND threshold through the JND model, and the target image area can be encoded, including motion estimation and residual prediction processes of video encoding.

[0081] It should be noted that traditional vector prediction usually involves finding a matching block with a zero or sufficiently small SAD (sum of abstract difference) for a luminance block (macroblock); however, from a visual perspective, SAD depends not only on its luminance amplitude but also on the local perceptual threshold. By considering objective distortion below the subjective perceptual threshold, the probability of zero appearing is increased, and the subsequent encoding bit rate is saved; at the same time, it also avoids deep searching when the change in the current luminance block (macroblock) is below the detectable level. Since this deep search is worthless, the encoding rate can be increased accordingly.

[0082] Furthermore, during the residual prediction process, for the current block (macroblock), residual values ​​within the entire block that do not exceed the perceptual threshold are set to 0. If the residuals within the entire block do not exceed the JND threshold, the block (macroblock) can be treated as an all-zero block, simplifying compression. If only some residuals are below the perceptual threshold, the variance of the DCT (Dual Clutch Transmission) coefficients will be reduced after passing through the residual filter. From the perspective of distortion rate, at a given bit rate, a low-variance signal will have a reconstructed signal with low objective distortion. If residuals above the perceptual threshold can sufficiently compensate for the loss of residuals below the perceptual threshold, then the application of the perceptual threshold can, in general, reduce both perceptual and objective distortion, ensuring that bit rate savings are achieved without compromising video quality.

[0083] Corresponding to the above method embodiment, this embodiment provides a device for determining the human eye perception threshold, such as Figure 5 As shown, the device includes:

[0084] An acquisition module 51 is configured to acquire first image parameters of a target image area; wherein the first image parameters include one or more of a brightness parameter, a texture parameter, and a color parameter;

[0085] A first determining module 52 is configured to determine a first perception threshold corresponding to the first image parameter based on a predetermined relationship between the image parameter and the perception threshold; wherein the relationship between the image parameter and the perception threshold is determined based on image quality evaluation parameter values ​​for images under various parameter values ​​of the image parameter and various noise conditions;

[0086] The second determining module 53 is configured to determine a perception threshold of the target image area according to the first perception threshold corresponding to the first image parameter.

[0087] An embodiment of the present invention provides a device for determining a human eye perception threshold, which obtains a first image parameter of a target image region; the first image parameter includes one or more of a brightness parameter, a texture parameter, and a color parameter; determines a first perception threshold corresponding to the first image parameter based on a predetermined relationship between the image parameter and the perception threshold; the relationship between the image parameter and the perception threshold is determined based on image quality evaluation parameter values ​​for images under various noise conditions at various parameter values ​​of the image parameter; and determines the perception threshold of the target image region based on the first perception threshold corresponding to the first image parameter. In this method, the perception threshold of the target image region is determined using the image quality evaluation parameter value. Compared to methods that measure the perception threshold through human visual observation, this method improves the efficiency of determining the human eye perception threshold, thereby reducing the bit rate during image encoding.

[0088] Furthermore, the relationship between the above-mentioned image parameters and the perception threshold is specifically determined by the following third determination module, including an original image acquisition unit, which is used to obtain an original image with a parameter value for each parameter value of the image parameter; a noise processing unit, which is used to add a variety of random noises of specified amplitudes to the original image for each original image, to obtain a noise image corresponding to each specified amplitude; and a relationship determination unit, which is used to determine the relationship between the image parameter and the perception threshold based on the image quality evaluation parameter value of the noise image and the image quality evaluation parameter value of the original image.

[0089] Furthermore, the above-mentioned relationship determination unit is also used to: determine the first image quality evaluation parameter value of the original image and the second image quality evaluation parameter values ​​of multiple noise images corresponding to the original image through the image quality evaluation model; determine the target image quality evaluation parameter value from the first image quality evaluation parameter value and the second image quality evaluation parameter value; and determine the relationship between the image parameter and the perception threshold based on the target image quality evaluation parameter value.

[0090] Furthermore, the above-mentioned relationship determination unit is also used to: obtain an intermediate image quality evaluation parameter value that is greater than or equal to the first image quality evaluation parameter value from the second image quality evaluation parameter value; and determine the maximum value among the intermediate image quality evaluation parameter values ​​as the target image quality evaluation parameter value.

[0091] Furthermore, the above-mentioned relationship determination unit is also used to: obtain a target noise image corresponding to the target image quality evaluation parameter value; determine the specified amplitude corresponding to the target noise image as the perception threshold corresponding to the parameter value; and determine the correspondence between each parameter value of the image parameter and the perception threshold corresponding to the parameter value as the relationship between the image parameter and the perception threshold.

[0092] Furthermore, the above-mentioned image parameters include brightness parameters, texture parameters and color parameters; the above-mentioned original image acquisition unit is also used to obtain an original grayscale image with grayscale values ​​for each grayscale value of the brightness parameter; wherein the grayscale value of each pixel in the original grayscale image is the same; for each texture value of the texture parameter, an original texture image with a texture value is obtained; wherein the texture value includes an average gradient value; for each color value of the color parameter, an original color image with a color value is obtained; wherein the color value includes an RGB value.

[0093] Furthermore, the above-mentioned first determination module is also used to: obtain the perception threshold corresponding to the first parameter value from the relationship between the predetermined image parameter and the perception threshold based on the first parameter value of the image parameter included in the first image parameter; and determine the perception threshold corresponding to the first parameter value as the first perception threshold.

[0094] Furthermore, the first image parameters include a plurality of parameters; and the second determining module is further configured to determine the maximum perception threshold among the first perception thresholds corresponding to each first image parameter as the perception threshold of the target image area.

[0095] Furthermore, the above-mentioned device also includes an encoding module, which is used to perform encoding processing on the target image area based on the perception threshold of the target image area.

[0096] The device for determining the human eye perception threshold provided in an embodiment of the present invention has the same technical features as the method for determining the human eye perception threshold provided in the above embodiment, and therefore can also solve the same technical problems and achieve the same technical effects.

[0097] This embodiment also provides an electronic device, including a processor and a memory, wherein the memory stores machine-executable instructions that can be executed by the processor, and the processor executes the machine-executable instructions to implement the above-mentioned method for determining the human eye perception threshold.

[0098] See also Figure 6 As shown, the electronic device includes a processor 100 and a memory 101. The memory 101 stores machine executable instructions that can be executed by the processor 100. The processor 100 executes the machine executable instructions to implement the above-mentioned method for determining the human eye perception threshold.

[0099] Further, Figure 6 The electronic device shown further includes a bus 102 and a communication interface 103 , and the processor 100 , the communication interface 103 and the memory 101 are connected via the bus 102 .

[0100] The memory 101 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. The communication connection between the system network element and at least one other network element is achieved through at least one communication interface 103 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used. The bus 102 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0101] The processor 100 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits in the processor 100 or software instructions. The above processor 100 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in conjunction with the embodiments of the present invention can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as a random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or register. The storage medium is located in the memory 101. The processor 100 reads the information in the memory 101 and, in conjunction with its hardware, completes the steps of the method of the aforementioned embodiment.

[0102] This embodiment also provides a machine-readable storage medium, which stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to implement the above-mentioned method for determining the human eye perception threshold.

[0103] The computer program product of the method, device and electronic device for determining the human eye perception threshold provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the method described in the previous method embodiments. For specific implementation, please refer to the method embodiments and will not be repeated here.

[0104] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems and devices can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0105] In addition, in the description of the embodiments of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to mechanical connections or electrical connections; they may refer to direct connections or indirect connections through an intermediate medium; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0106] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0107] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0108] Finally, it should be noted that the above embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for determining the human eye perception threshold, characterized in that: include: Acquiring first image parameters of a target image area; wherein the first image parameters include one or more of a brightness parameter, a texture parameter, and a color parameter; if the target image area is a single-channel image, the first image parameters include one of the brightness parameter, the texture parameter, and the color parameter; if the target image area is a multi-channel image, the first image parameters include multiple of the brightness parameter, the texture parameter, and the color parameter; Determining a first perception threshold corresponding to the first image parameter based on a predetermined relationship between the image parameter and the perception threshold; wherein the relationship between the image parameter and the perception threshold is determined based on image quality evaluation parameter values ​​of images under various parameter values ​​of the image parameter and various noise conditions; The perception threshold of the target image area is determined according to the first perception threshold corresponding to the first image parameter; wherein, if the first image parameter includes one of the brightness parameter, the texture parameter and the color parameter, the first perception threshold corresponding to the first image parameter is determined as the perception threshold of the target image area; if the first image parameter includes multiple types of the brightness parameter, the texture parameter and the color parameter, the maximum value or the minimum value of the first perception threshold corresponding to each of the first image parameters is determined as the perception threshold of the target image area; or, the average value of the first perception threshold corresponding to each of the first image parameters is determined as the perception threshold of the target image area; or, the sum of the first perception thresholds corresponding to each of the first image parameters is determined as the perception threshold of the target image area.

2. The method according to claim 1, characterized in that The relationship between the image parameter and the perception threshold is specifically determined in the following manner: For each parameter value of the image parameter, obtaining an original image having the parameter value; For each of the original images, adding a plurality of random noises of specified amplitudes to the original image to obtain a noise image corresponding to each of the specified amplitudes; Based on the image quality evaluation parameter value of the noise image and the image quality evaluation parameter value of the original image, a relationship between the image parameter and the perception threshold is determined.

3. The method according to claim 2, characterized in that The step of determining the relationship between the image parameter and the perception threshold based on the image quality evaluation parameter value of the noise image and the image quality evaluation parameter value of the original image includes: Determining, by an image quality evaluation model, a first image quality evaluation parameter value of the original image and second image quality evaluation parameter values ​​of a plurality of noise images corresponding to the original image; determining a target image quality evaluation parameter value from the first image quality evaluation parameter value and the second image quality evaluation parameter value; Based on the target image quality evaluation parameter value, a relationship between the image parameter and a perception threshold is determined.

4. The method according to claim 3, characterized in that The step of determining a target image quality evaluation parameter value from the first image quality evaluation parameter value and the second image quality evaluation parameter value comprises: acquiring, from the second image quality evaluation parameter values, an intermediate image quality evaluation parameter value that is greater than or equal to the first image quality evaluation parameter value; The maximum value among the intermediate image quality evaluation parameter values ​​is determined as the target image quality evaluation parameter value.

5. The method according to claim 3, characterized in that The step of determining the relationship between the image parameter and the perception threshold based on the target image quality evaluation parameter value comprises: Acquiring a target noise image corresponding to the target image quality evaluation parameter value; determining a specified amplitude corresponding to the target noise image as a perception threshold corresponding to the parameter value; The corresponding relationship between each parameter value of the image parameter and the perception threshold corresponding to the parameter value is determined as the relationship between the image parameter and the perception threshold.

6. The method according to claim 2, characterized in that The image parameters include brightness parameters, texture parameters, and color parameters; for each parameter value of the image parameters, the step of obtaining an original image having the parameter value includes: For each grayscale value of the brightness parameter, obtaining an original grayscale image having the grayscale value; wherein the grayscale value of each pixel in the original grayscale image is the same; For each texture value of the texture parameter, obtaining an original texture image having the texture value; wherein the texture value includes an average gradient value; For each color value of the color parameter, an original color image having the color value is obtained; wherein the color value includes an RGB value.

7. The method according to claim 1, characterized in that The step of determining a first perception threshold corresponding to the first image parameter based on a predetermined relationship between the image parameter and the perception threshold includes: According to a first parameter value of an image parameter included in the first image parameter, obtaining a perception threshold corresponding to the first parameter value from a relationship between the predetermined image parameter and the perception threshold; The perception threshold corresponding to the first parameter value is determined as the first perception threshold.

8. The method according to claim 1, characterized in that The first image parameters include a plurality of parameters; and the step of determining the perception threshold of the target image area according to the first perception threshold corresponding to the first image parameters includes: The maximum perception threshold among the first perception thresholds corresponding to each of the first image parameters is determined as the perception threshold of the target image area.

9. The method according to claim 1, characterized in that After the step of determining the perception threshold of the target image area according to the first perception threshold corresponding to the first image parameter, the method further includes: performing encoding processing on the target image area based on the perception threshold of the target image area.

10. A device for determining a human eye perception threshold, characterized in that: include: an acquisition module, configured to acquire first image parameters of a target image region; wherein the first image parameters include one or more of a brightness parameter, a texture parameter, and a color parameter; if the target image region is a single-channel image, the first image parameters include one of the brightness parameter, the texture parameter, and the color parameter; if the target image region is a multi-channel image, the first image parameters include multiple of the brightness parameter, the texture parameter, and the color parameter; a first determining module, configured to determine a first perception threshold corresponding to the first image parameter based on a predetermined relationship between the image parameter and the perception threshold; wherein the relationship between the image parameter and the perception threshold is determined based on image quality evaluation parameter values ​​of images under various parameter values ​​of the image parameter and various noise conditions; The second determination module is used to determine the perception threshold of the target image area based on the first perception threshold corresponding to the first image parameter; wherein, if the first image parameter includes one of the brightness parameter, the texture parameter and the color parameter, the first perception threshold corresponding to the first image parameter is determined as the perception threshold of the target image area; if the first image parameter includes multiple types of the brightness parameter, the texture parameter and the color parameter, the maximum value or the minimum value of the first perception threshold corresponding to each of the first image parameters is determined as the perception threshold of the target image area; or, the average value of the first perception threshold corresponding to each of the first image parameters is determined as the perception threshold of the target image area; or, the sum of the first perception thresholds corresponding to each of the first image parameters is determined as the perception threshold of the target image area.

11. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores machine executable instructions that can be executed by the processor, and the processor executes the machine executable instructions to implement the method for determining the human eye perception threshold according to any one of claims 1 to 9.

12. A machine-readable storage medium, characterized in that The machine-readable storage medium stores machine-executable instructions. When the machine-executable instructions are called and executed by the processor, the machine-executable instructions prompt the processor to implement the method for determining the human eye perception threshold as described in any one of claims 1 to 9.

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