Audio and video quality enhancement method and device based on machine learning, equipment and medium

Abstract

The invention discloses an audio and video quality enhancement method and device based on machine learning, equipment and a medium, and relates to the technical field of audio and video quality enhancement, and the method comprises the steps: if to-be-processed initial audio and video information is received, determining to-be-processed audio and video data according to the initial audio and video information and preset machine learning parameters; determining audio and video processing data according to the to-be-processed audio and video data and a preset machine learning database, the audio and video processing data including audio processing data and video processing data; when the audio and video processing data is the audio processing data, determining audio quality enhancement data according to the audio processing data and a preset first optimization rule; and when the audio and video processing data is the video processing data, determining video quality enhancement data according to the video processing data and a preset second optimization rule. And the audio and video playing effect is improved.

Description

Technical Field

[0001] The present application relates to the technical field of audio and video quality enhancement, and in particular to a method, device, equipment and medium for audio and video quality enhancement based on machine learning. Background Art

[0002] At present, with the development of audio and video related technologies, higher requirements are also placed on audio and video processing methods.

[0003] The traditional audio and video processing method is to directly convert the initial audio and video into an electrical signal that can be transmitted, and then convert the electrical signal into audio and video that can be played when the audio and video needs to be played. This audio and video processing method has great defects. Because the initial audio and video are directly converted into an electrical signal that can be transmitted, the initial audio and video itself has a poor playback effect that affects the final playback effect. That is, this audio and video processing method will affect the final playback effect due to the poor playback effect of the initial audio and video itself, thereby causing poor audio and video playback effect.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a method, device, equipment and medium for enhancing audio and video quality based on machine learning, aiming to solve the technical problem of poor audio and video playback effect.

[0006] To achieve the above objectives, the present application proposes a method for enhancing audio and video quality based on machine learning, and the method for enhancing audio and video quality based on machine learning includes: If the initial audio and video information to be processed is received, the audio and video data to be processed is determined according to the initial audio and video information and the preset machine learning parameters; Determining audio and video processing data according to the audio and video data to be processed and a preset machine learning database, wherein the audio and video processing data includes audio processing data and video processing data; When the audio and video processing data is the audio processing data, determining audio quality enhancement data according to the audio processing data and a preset first optimization rule; When the audio and video processing data is the video processing data, the video quality enhancement data is determined according to the video processing data and a preset second optimization rule.

[0007] In one embodiment, the preset machine learning parameters include a standard video parameter threshold and a standard audio parameter threshold, and the step of determining the audio and video data to be processed according to the initial audio and video information and the preset machine learning parameters includes: When the initial audio and video information is audio information, detecting whether an audio parameter value in the audio information meets the standard audio parameter threshold, and when the audio parameter value in the audio information does not meet the standard audio parameter threshold, using audio data in the audio information as audio and video data to be processed; When the initial audio and video information is video information, it is detected whether the video parameter value in the video information meets the standard video parameter threshold. When the video parameter value in the video information does not meet the standard video parameter threshold, the video data in the video information is used as the audio and video data to be processed.

[0008] In one embodiment, the preset machine learning database includes an audio coding transmission library and a video coding transmission library, and the step of determining the audio and video processing data according to the audio and video data to be processed and the preset machine learning database includes: Determine audio data in the audio and video data to be processed, determine a first encoding and transmission mode corresponding to the data characteristics of the audio data in the audio encoding and transmission library, and bind the first encoding and transmission mode to the audio data as audio processing data; Determine the video data in the audio and video data to be processed, determine a second encoding and transmission method corresponding to the data characteristics of the video data in the video encoding and transmission library, and bind the second encoding and transmission method to the video data as video processing data.

[0009] In one embodiment, the preset first optimization rule includes an audio noise reduction rule, an audio equalization rule, and an echo cancellation rule, and the step of determining the audio quality enhancement data according to the audio processing data and the preset first optimization rule includes: When the noise value of the audio processing data is greater than a preset audio noise reduction threshold, the audio processing data is denoised based on the audio noise reduction rule to obtain noise-reduced audio data, and when the equalization value of the audio processing data is greater than a preset audio equalization threshold, the noise-reduced audio data is audio-equalized based on the audio equalization rule to obtain equalized audio data; When the echo value of the audio processing data is greater than a preset echo cancellation threshold, performing echo cancellation on the equalized audio data based on the echo cancellation rule to obtain audio quality enhanced data; When the echo value of the audio processing data is less than or equal to a preset echo cancellation threshold, the equalized audio data is used as audio quality enhancement data.

[0010] In one embodiment, the preset second optimization rule includes a video denoising rule, a contrast enhancement rule, a sharpening processing rule, and a resolution processing rule, and the step of determining the video quality enhancement data according to the video processing data and the preset second optimization rule includes: When the noise value of the video processing data is greater than a preset video denoising threshold, denoising the video processing data based on the video denoising rule to obtain denoised video data; When the contrast of the video processing data is less than or equal to a preset contrast enhancement threshold, using the noise reduction video data as video quality enhancement data; When the contrast of the video processing data is greater than a preset contrast enhancement threshold, the noise reduction video data is contrast adjusted based on the contrast enhancement rule to obtain contrast adjustment data, and when the sharpening value of the contrast adjustment data is greater than a preset sharpening processing threshold and the resolution of the contrast adjustment data is greater than a preset resolution processing threshold, the contrast adjustment data is processed based on the sharpening processing rule and the resolution processing rule to obtain video quality enhanced data.

[0011] In one embodiment, the method for enhancing audio and video quality based on machine learning further includes: Acquire initial training audio and video, and determine a playback score corresponding to the initial training audio and video, wherein the playback score includes a first score value for direct playback; Determine an audio noise reduction threshold, an audio equalization threshold, and an echo cancellation threshold corresponding to the audio data under the first scoring value, and use the audio noise reduction threshold, the audio equalization threshold, and the echo cancellation threshold as a basis for starting a preset first optimization rule; Determine the video denoising threshold, contrast enhancement threshold, sharpening processing threshold and resolution processing threshold corresponding to the video data under the first scoring value, and use the video denoising threshold, the contrast enhancement threshold, the sharpening processing threshold and the resolution processing threshold as the basis for starting a preset second optimization rule.

[0012] In one embodiment, the playback score also includes a second score value played after transmission, and after the step of determining the playback score corresponding to the initial training audio and video, the following steps are included: Determine a first correspondence between the audio encoding method and the data feature of the audio data under the second scoring value, and use the first correspondence as an audio encoding transmission library in a preset machine learning database; A second corresponding relationship between the video encoding method and the data feature of the video data under the second scoring value is determined, and the second corresponding relationship is used as a video encoding transmission library in a preset machine learning database.

[0013] In addition, to achieve the above purpose, the present application also proposes an audio and video quality enhancement device based on machine learning, and the audio and video quality enhancement device based on machine learning includes: An information acquisition module, for determining the audio and video data to be processed according to the initial audio and video information and preset machine learning parameters if the initial audio and video information to be processed is received; A type determination module, used to determine audio and video processing data according to the audio and video data to be processed and a preset machine learning database, wherein the audio and video processing data includes audio processing data and video processing data; A first enhancement module, configured to determine audio quality enhancement data according to the audio processing data and a preset first optimization rule when the audio and video processing data is the audio processing data; The second enhancement module is used to determine video quality enhancement data according to the video processing data and a preset second optimization rule when the audio and video processing data is the video processing data.

[0014] In addition, to achieve the above-mentioned objectives, the present application also proposes an audio and video quality enhancement device based on machine learning, and the audio and video quality enhancement device based on machine learning includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the audio and video quality enhancement method based on machine learning as described above.

[0015] In addition, to achieve the above-mentioned purpose, the present application also proposes a medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the audio and video quality enhancement method based on machine learning as described above are implemented.

[0016] The embodiment of the present application provides an audio and video quality enhancement method based on machine learning, which comprises the following steps: if initial audio and video information to be processed is received, the audio and video data to be processed is determined according to the initial audio and video information and preset machine learning parameters; the audio and video processing data is determined according to the audio and video data to be processed and a preset machine learning database, wherein the audio and video processing data includes audio processing data and video processing data; when the audio and video processing data is the audio processing data, the audio quality enhancement data is determined according to the audio processing data and a preset first optimization rule; when the audio and video processing data is the video processing data, the video quality enhancement data is determined according to the video processing data and a preset second optimization rule. This audio and video quality enhancement method based on machine learning is performed by initial audio and video information to be processed. The audio and video information and the preset machine learning parameters determine the audio and video data to be processed, and then the audio and video data are processed and the preset machine learning database determines the audio and video processing data. Finally, under different processing data types, the audio processing data and the preset first optimization rule determine the audio quality enhancement data and / or determine the video quality enhancement data according to the video processing data and the preset second optimization rule, thereby avoiding directly converting the initial audio and video into electrical signals that can be transmitted, and the phenomenon that the initial audio and video itself has poor playback effect affecting the final playback effect. Under different processing data types, the audio processing data and the preset first optimization rule determine the audio quality enhancement data and / or determine the video quality enhancement data according to the video processing data and the preset second optimization rule, thereby improving the audio and video playback effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of a first embodiment of the method for enhancing audio and video quality based on machine learning of the present application; Figure 2 This is a schematic diagram of an implementation flow of the audio and video quality enhancement method based on machine learning in this application; Figure 3 This is a flow chart of a second embodiment of the method for enhancing audio and video quality based on machine learning of the present application; Figure 4 This is a module diagram of the device for enhancing audio and video quality based on machine learning in this application; Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in this application.

[0018] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0019] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0020] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0021] The existing audio and video processing method is to directly convert the initial audio and video into an electrical signal that can be transmitted, and convert the electrical signal into audio and video that can be played when the audio and video needs to be played. That is, the audio and video in different environments or at different time points are directly processed into electrical signals to transmit the electrical signals to the area where they need to be played, and then the electrical signals are processed into playable audio and video signals for playback. The entire process does not take into account the impact of different actual environments or different time points and internal signal acquisition on audio and video, and the problem of poor playback effect of the initial audio and video itself affecting the final playback effect, such as low contrast in the environment itself, that is, unclear information shot under strong light, and noisy ambient sound, resulting in large noise in the sound signal.

[0022] Therefore, based on the deficiencies of the above audio and video quality processing schemes, the present application proposes an audio and video quality enhancement method based on machine learning. The solution of the embodiment of the present application is: determine the audio and video data to be processed through the initial audio and video information and the preset machine learning parameters, and then process the audio and video data and the preset machine learning database to determine the audio and video processing data, and finally, under different processing data types, the audio processing data and the preset first optimization rule determine the audio quality enhancement data and or determine the video quality enhancement data according to the video processing data and the preset second optimization rule, and avoid directly converting the initial audio and video into an electrical signal that can be transmitted, and there is a phenomenon that the initial audio and video itself has a poor playback effect that affects the final playback effect. Under different processing data types, the audio processing data and the preset first optimization rule can determine the audio quality enhancement data and or determine the video quality enhancement data according to the video processing data and the preset second optimization rule, thereby improving the audio and video playback effect.

[0023] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or a device capable of realizing the above functions, a controller, etc. The following takes the controller as an example to illustrate this embodiment and the following embodiments.

[0024] Based on this, the embodiment of the present application provides an audio and video quality enhancement method based on machine learning, referring to Figure 1 , Figure 1 This is a flowchart of the first embodiment of the audio and video quality enhancement method based on machine learning in this application.

[0025] Reference Figure 1The present application provides a method for enhancing audio and video quality based on machine learning. In a first embodiment of the method for enhancing audio and video quality based on machine learning, the method for enhancing audio and video quality based on machine learning includes: Step S10, if the initial audio and video information to be processed is received, determining the audio and video data to be processed according to the initial audio and video information and the preset machine learning parameters; Step S20, determining audio and video processing data according to the audio and video data to be processed and a preset machine learning database, wherein the audio and video processing data includes audio processing data and video processing data; In this embodiment, upon receiving the initial audio and video information to be processed, the initial audio and video information will be judged to determine whether the initial audio and video information needs to be processed. The main judgment method is to determine whether the initial audio and video information needs to be quality enhanced based on the preset machine learning parameters, so that when it is determined that quality enhancement is required, the initial audio and video information is used as the audio and video data to be processed. Among them, the audio and video data to be processed refers to the audio and video data that needs to be quality enhanced, the initial audio and video information refers to the audio and video initially collected or acquired, and the preset machine learning parameters refer to the parameters that need to be quality enhanced based on machine learning. For example, for video data, when the contrast is less than a certain value, it is determined that the video data needs to be quality enhanced. For example, for audio data, when the noise is greater than a certain value, it is determined that the audio data needs to be quality enhanced. After determining the audio and video data to be processed that needs to be quality enhanced, the audio and video processing data will be determined based on the audio and video data to be processed and the preset machine learning database, wherein the audio and video processing data includes audio processing data and video processing data, and the preset machine learning database refers to a database determined based on machine learning, which includes at least an audio encoding and transmission library and a video encoding and transmission library, the audio encoding and transmission library refers to a library that defines audio encoding and transmission, and the video encoding and transmission library refers to a library that defines video encoding and transmission, audio processing data refers to audio data after encoding and transmission processing, and video processing data refers to video data after encoding and transmission processing, thereby avoiding the influence of audio and video data on encoding and transmission to ensure the audio and video playback effect.

[0026] Step S30, when the audio and video processing data is the audio processing data, determining audio quality enhancement data according to the audio processing data and a preset first optimization rule; Step S40, when the audio and video processing data is the video processing data, determine video quality enhancement data according to the video processing data and a preset second optimization rule.

[0027] In this embodiment, after the audio and video processing data is determined, the audio processing data and the video processing data in the audio and video processing data will be processed respectively, that is, the audio processing data and the video processing data will be processed respectively according to certain processing rules to obtain the corresponding quality enhancement data, such as determining the audio quality enhancement data based on the audio processing data and the preset first optimization rule, and or determining the video quality enhancement data based on the video processing data and the preset second optimization rule. Among them, the audio quality enhancement data refers to the audio data after the data itself is processed, the video quality enhancement data refers to the video data after the data itself is processed, the preset first optimization rule refers to the rule for processing the audio processing data, such as the audio noise reduction rule, the audio equalization rule and the echo cancellation rule, and the preset second optimization rule refers to the rule for processing the video processing data, such as the video denoising rule, the contrast enhancement rule, the sharpening processing rule and the resolution processing rule, and finally the audio and video data after quality enhancement can be obtained, so that the effect of the audio and video data playback can be guaranteed by playing based on the audio and video data. It is worth noting that the order of processing based on respective rules and processing based on machine learning database can be swapped, that is, the quality enhancement data of audio and video can be obtained directly based on the audio and video data to be processed, and then the quality enhancement data of audio and video can be encoded and transmitted with the machine learning database as the final audio and video quality enhancement data for subsequent audio and video data playback processing, thereby improving the audio and video data playback effect from the aspects of the data itself and transmission programming.

[0028] In one embodiment, referring to Figure 2 , Figure 2 This is a schematic diagram of an implementation flow of the audio and video quality enhancement method based on machine learning in the present application. After obtaining the audio and video that needs to be processed, it will be determined whether the audio and video needs to be processed (quality enhancement processing), that is, the step of determining the audio and video data to be processed according to the initial audio and video information and the preset machine learning parameters is executed, so as to give priority to the improvement and optimization of transmission when it is determined that processing is required, that is, the audio and video processing data is determined based on the preset machine learning database, so as to achieve the improvement and optimization of transmission through the improvement of encoded transmission, and then after determining the improvement and optimization of transmission, the data itself is further optimized to improve the data effect, that is, at this time, the audio and video data is processed based on their respective rules to improve the defects of the audio and video data, so as to achieve the purpose of improving the audio and video playback effect.

[0029] In the present embodiment, a method for enhancing audio and video quality based on machine learning is provided, wherein if initial audio and video information to be processed is received, audio and video data to be processed is determined according to the initial audio and video information and preset machine learning parameters; audio and video processing data is determined according to the audio and video data to be processed and a preset machine learning database, wherein the audio and video processing data includes audio processing data and video processing data; when the audio and video processing data is the audio processing data, audio quality enhancement data is determined according to the audio processing data and a preset first optimization rule; when the audio and video processing data is the video processing data, video quality enhancement data is determined according to the video processing data and a preset second optimization rule. This method for enhancing audio and video quality based on machine learning is provided by initial audio and video information to be processed. The audio and video information and the preset machine learning parameters determine the audio and video data to be processed, and then the audio and video data are processed and the preset machine learning database determines the audio and video processing data. Finally, under different processing data types, the audio processing data and the preset first optimization rule determine the audio quality enhancement data and / or determine the video quality enhancement data according to the video processing data and the preset second optimization rule, thereby avoiding directly converting the initial audio and video into electrical signals that can be transmitted, and the phenomenon that the initial audio and video itself has poor playback effect affecting the final playback effect. Under different processing data types, the audio processing data and the preset first optimization rule determine the audio quality enhancement data and / or determine the video quality enhancement data according to the video processing data and the preset second optimization rule, thereby improving the audio and video playback effect.

[0030] Further, based on the above-mentioned first embodiment of the present application, a second embodiment of the audio and video quality enhancement method based on machine learning of the present application is proposed. In this embodiment, in the above-mentioned step S10, the preset machine learning parameters include a standard video parameter threshold and a standard audio parameter threshold, and the step of determining the audio and video data to be processed according to the initial audio and video information and the preset machine learning parameters includes: Step S11, when the initial audio and video information is audio information, detecting whether the audio parameter value in the audio information meets the standard audio parameter threshold, and when the audio parameter value in the audio information does not meet the standard audio parameter threshold, using the audio data in the audio information as the audio and video data to be processed; Step S12, when the initial audio and video information is video information, detect whether the video parameter value in the video information meets the standard video parameter threshold value, and when the video parameter value in the video information does not meet the standard video parameter threshold value, use the video data in the video information as the audio and video data to be processed.

[0031] In this embodiment, when determining whether quality enhancement processing is required, a judgment will be made based on the standard video parameter threshold and the standard audio parameter threshold in the preset machine learning parameters, respectively, wherein the standard video parameter threshold refers to a user-defined parameter of the video data when no quality enhancement processing is required, which may be the contrast, light intensity, etc. of the video data. If the contrast is outside the first area and the light intensity is outside the first value, it is determined that the video data needs to be quality enhanced. The standard audio parameter threshold refers to a user-defined parameter of the audio data when no quality enhancement processing is required, which may be the noise, echo, etc. of the audio data. If the noise is outside a second value or the echo is outside a third value, it is determined that the audio data needs to be quality enhanced. At this time, the initial audio and video information is divided into audio information containing audio data and video information containing video data for processing. When it is audio information, the opportunity detects whether the audio parameter value in the audio information meets the standard audio parameter threshold, so that when the audio parameter value in the audio information does not meet the standard audio parameter threshold, the audio data in the audio information is used as the audio and video data to be processed, wherein the audio parameter value refers to the relevant parameters of the audio data, which can be a noise value, an echo value, etc., which can be directly determined based on the audio parameter, and not meeting the standard audio parameter threshold means that the noise is outside a second value, or the echo is outside a third value; when it is video information, the opportunity detects whether the video parameter value in the video information meets the standard video parameter threshold, so that when the video parameter value in the video information does not meet the standard video parameter threshold, the video data in the video information is used as the audio and video data to be processed, wherein the video parameter value refers to the relevant parameters of the video data, which can be contrast, light intensity, etc., which can be directly determined based on the video parameter, and not meeting the standard video parameter threshold means that the contrast is outside the first area and the light intensity is outside the first value. To determine whether quality enhancement processing is required, thereby ensuring the effect of subsequent audio and video playback.

[0032] Furthermore, the preset machine learning database includes an audio coding transmission library and a video coding transmission library, and the step of determining the audio and video processing data according to the audio and video data to be processed and the preset machine learning database includes: Step S21, determining audio data in the audio and video data to be processed, determining a first encoding transmission mode corresponding to the data characteristics of the audio data in the audio encoding transmission library, and binding the first encoding transmission mode with the audio data as audio processing data; Step S22, determining the video data in the audio and video data to be processed, determining a second encoding transmission method corresponding to the data characteristics of the video data in the video encoding transmission library, and binding the second encoding transmission method with the video data as video processing data.

[0033] In this embodiment, when optimizing the encoding and transmission of the audio and video data to be processed, the audio data (data related to audio playback) in the audio and video data to be processed will be determined respectively, and then on the one hand, the first encoding and transmission mode corresponding to the data feature of the audio data is determined in the audio encoding and transmission library, and the first encoding and transmission mode is bound to the audio data as audio processing data, wherein the audio encoding and transmission library refers to a database that defines encoding and transmission modes corresponding to different audio features, the first encoding and transmission mode refers to the encoding and transmission mode that matches the data feature of the audio data, and the audio feature refers to the feature of the audio data, such as the wavelength and frequency of the audio data; on the other hand, the second encoding and transmission mode corresponding to the data feature of the video data is determined in the video encoding and transmission library, and the second encoding and transmission mode is bound to the video data as video processing data, wherein the video encoding and transmission library refers to a database that defines encoding and transmission modes corresponding to different video features, the second encoding and transmission mode refers to the encoding and transmission mode that matches the data feature of the video data, and the video feature refers to the feature of the video data, such as the wavelength and frequency of the video data, so that the playback effect of the audio and video data can be improved from the encoding and transmission mode.

[0034] In one embodiment, on the one hand, suitable encoding settings and algorithms are selected for the audio and video features of different audio and video data to improve the compression efficiency and transmission quality of audio and video data. Encoding optimization can reduce the redundancy and distortion of audio and video data, improve data transmission efficiency and viewing experience. On the other hand, advanced transmission technologies and protocols such as real-time transport protocol (RTP) and real-time streaming protocol (RTSP) are used to improve the transmission speed and stability of audio and video data. Transmission optimization can reduce the delay and packet loss rate of audio and video data, and improve the real-time and reliability of data.

[0035] Further, based on the first embodiment and / or the second embodiment of the present application, a third embodiment of the audio and video quality enhancement method based on machine learning of the present application is proposed. In this embodiment, in the above step S30, the preset first optimization rule includes an audio noise reduction rule, an audio equalization rule, and an echo cancellation rule, and the step of determining the audio quality enhancement data according to the audio processing data and the preset first optimization rule includes: Step S31, when the noise value of the audio processing data is greater than a preset audio noise reduction threshold, the audio processing data is denoised based on the audio noise reduction rule to obtain noise-reduced audio data, and when the equalization value of the audio processing data is greater than a preset audio equalization threshold, the noise-reduced audio data is audio-equalized based on the audio equalization rule to obtain equalized audio data; Step S32, when the echo value of the audio processing data is greater than a preset echo cancellation threshold, performing echo cancellation on the equalized audio data based on the echo cancellation rule to obtain audio quality enhanced data; Step S33: When the echo value of the audio processing data is less than or equal to a preset echo cancellation threshold, use the equalized audio data as audio quality enhancement data.

[0036] In this embodiment, when the audio processing data is processed, the audio processing data will be processed based on the audio noise reduction rules, audio equalization rules and echo cancellation rules. The audio noise reduction rules refer to the defined rules for noise reduction of audio data, which can be the commonly used audio noise reduction method. The audio equalization rules refer to the defined rules for audio equalization of audio data, which can be the commonly used audio equalization method. The echo cancellation rules refer to the defined rules for echo cancellation of audio data, which can be the commonly used echo cancellation method. When the noise value of the audio processing data is greater than a preset audio noise reduction threshold, the audio processing data will be denoised based on the audio noise reduction rules to obtain noise-reduced audio data, and when the equalization value of the audio processing data is greater than the preset audio equalization threshold, the noise-reduced audio data will be audio-equalized based on the audio equalization rules to obtain equalized audio data, wherein the preset audio noise reduction threshold refers to the noise value of the best audio noise reduction defined by the user based on machine learning. If it exceeds the preset audio noise reduction threshold A, it is determined that noise reduction is required, and the noise reduction audio data refers to the audio data after noise reduction, and the preset audio equalization threshold refers to the equalization value of the best audio equalization defined by the user based on machine learning. If it exceeds the preset audio equalization threshold B, it is determined that audio equalization is required, and the equalized audio data refers to the audio data after noise reduction and audio equalization. It is worth noting that the noise value of the audio processing data, the equalization value of the audio processing data, and the echo value of the audio processing data can all be obtained based on conventional methods and are not limited here. Finally, when the echo value of the audio processing data is less than or equal to the preset echo cancellation threshold, that is, there is no echo influence at this time, the equalized audio data can be directly used as the audio quality enhancement data. On the contrary, when the echo value of the audio processing data is greater than the preset echo cancellation threshold, the equalized audio data is echo-cancelled based on the echo cancellation rule to obtain the audio quality enhancement data. The preset echo cancellation threshold refers to the best echo cancellation value defined by the user based on machine learning. If it exceeds the preset echo cancellation threshold C, it is determined that echo cancellation is required. The audio quality enhancement data refers to the audio data after echo cancellation, noise reduction and audio equalization. It is worth noting that the three processing flows can be interchanged in order, which is not limited here.

[0037] In one embodiment, audio noise reduction also utilizes filters or other methods to remove noise from the audio to achieve a preset audio noise reduction threshold range. The noise of the audio data may come from environmental noise, equipment noise, etc. Through noise reduction processing, the audio can be made clearer and purer. Audio equalization refers to adjusting the frequency response characteristics of the audio so that the audio is balanced at different frequencies. Audio equalization can improve the sound quality and listening experience of the audio, making it more pleasant and comfortable. In audio communications, echo is a common problem. Echo cancellation technology can remove echo components in the audio. By analyzing the echo characteristics in the audio signal and eliminating or weakening it, the audio clarity and call quality can be improved. The audio playback effect is guaranteed through the above processing method.

[0038] In one embodiment, the preset second optimization rule includes a video denoising rule, a contrast enhancement rule, a sharpening processing rule, and a resolution processing rule, and the step of determining the video quality enhancement data according to the video processing data and the preset second optimization rule includes: Step S41, when the noise value of the video processing data is greater than a preset video denoising threshold, denoising the video processing data based on the video denoising rule to obtain denoised video data; Step S42, when the contrast of the video processing data is less than or equal to a preset contrast enhancement threshold, using the noise reduction video data as video quality enhancement data; Step S43, when the contrast of the video processing data is greater than a preset contrast enhancement threshold, the noise reduction video data is contrast adjusted based on the contrast enhancement rule to obtain contrast adjustment data, and when the sharpening value of the contrast adjustment data is greater than a preset sharpening processing threshold and the resolution of the contrast adjustment data is greater than a preset resolution processing threshold, the contrast adjustment data is processed based on the sharpening processing rule and the resolution processing rule to obtain video quality enhanced data.

[0039] In this embodiment, when processing video processing data, the video processing data will be processed based on the video denoising rule, contrast enhancement rule, sharpening processing rule and resolution processing rule. The video denoising rule refers to the rule defined for denoising video data, which can be a commonly used video denoising method. The contrast enhancement rule refers to the rule defined for adjusting the contrast of video data, which can be a commonly used contrast adjustment method. The sharpening processing rule refers to the rule defined for sharpening video data, which can be a commonly used sharpening processing method. The resolution processing rule refers to the rule defined for resolution processing of video data, which can be a commonly used resolution processing method. When the noise value of the video processing data is greater than the preset video denoising threshold, the video processing data will be denoised based on the video denoising rule to obtain denoised video data, and subsequent processing will be performed based on the contrast of the audio processing data, wherein the preset video denoising threshold refers to the noise value of the best video denoising defined by the user based on machine learning. If it exceeds the preset video denoising threshold D, it is determined that denoising is required, and the denoised video data refers to the video data after denoising. At this time, it is necessary to define contrast processing after denoising to ensure the accuracy of the entire data before processing the data, thereby improving the accuracy of subsequent video data processing. At this time, if the contrast of the video processing data is less than or equal to the preset contrast enhancement threshold, the noise reduction video data is used as video quality enhancement data. The contrast enhancement threshold refers to the optimal contrast value defined by the user based on machine learning. If it exceeds the preset contrast enhancement threshold E, it is determined that contrast adjustment is required, generally contrast enhancement.

[0040] On the other hand, when the contrast of the video processing data is greater than a preset contrast enhancement threshold, the contrast of the noise reduction video data is adjusted based on the contrast enhancement rule to obtain contrast adjustment data, and when the sharpening value of the contrast adjustment data is greater than the preset sharpening processing threshold, and the resolution of the contrast adjustment data is greater than the preset resolution processing threshold, the contrast adjustment data is processed based on the sharpening processing rule and the resolution processing rule to obtain video quality enhancement data, wherein the preset sharpening processing threshold refers to the optimal sharpening value defined by the user based on machine learning. If it exceeds the preset sharpening processing threshold F, it is determined that sharpening processing is required. The preset resolution processing threshold refers to the optimal resolution value defined by the user based on machine learning. If it exceeds the preset resolution processing threshold G, it is determined that resolution processing is required. The video quality enhancement data refers to the video data after the above noise reduction and contrast processing (at least including contrast enhancement, sharpening and resolution processing). It is worth noting that the noise value of the video processing data and the contrast of the video processing data, as well as the sharpening value and resolution of the video processing data can be obtained based on conventional methods, which are not limited here. The three processing flows after the contrast analysis can be interchanged in order, which are not limited here.

[0041] In one embodiment, the video denoising rule can use filters or other methods to reduce noise interference in the image. Noise is usually caused by electromagnetic interference, signal attenuation and other factors during signal transmission. By using advanced noise reduction algorithms and filters, these noises can be effectively suppressed and removed to make the picture clearer and more delicate. The contrast enhancement rule refers to adjusting the brightness and contrast of the video to make the image more vivid and vivid. Contrast refers to the degree of brightness change in the image. By optimizing the contrast, the dynamic range of the image can be increased and the details and texture presentation of the image can be improved. The sharpening processing rule makes the image sharper by increasing the contrast of edges and details. Sharpening processing helps to highlight the details in the image and make the picture clearer. The resolution processing rule uses advanced technologies such as deep learning to enhance low-resolution videos to higher resolutions. Super-resolution processing can restore more image details and improve the clarity of the video. It is worth noting that before or after the video denoising rule (before judging the contrast), the video data can also be defogged, that is, the haze video can be processed to reduce the interference of haze and improve the clarity of the image. The defogging algorithm usually analyzes the fog component in the image and removes or weakens it to restore a clear image. The video data can also be processed for motion compensation. For fast-moving videos, the motion trajectory is analyzed by the algorithm to correct the image blur. The motion compensation technology can compensate for the image blur and distortion caused by movement, making the picture smoother and clearer. The above processing methods can ensure the video playback effect.

[0042] Further, based on the first embodiment, the second embodiment and / or the third embodiment of the present application, a fourth embodiment of the method for enhancing audio and video quality based on machine learning is proposed. In this embodiment, referring to Figure 3 , Figure 3 This is a flow chart of a second embodiment of the method for enhancing audio and video quality based on machine learning of the present application, wherein the method for enhancing audio and video quality based on machine learning further includes: Step S50, obtaining initial training audio and video, and determining a playback score corresponding to the initial training audio and video, wherein the playback score includes a first score value for direct playback; Step S60, determining an audio noise reduction threshold, an audio equalization threshold, and an echo cancellation threshold corresponding to the audio data under the first scoring value, and using the audio noise reduction threshold, the audio equalization threshold, and the echo cancellation threshold as a basis for starting a preset first optimization rule; Step S70, determine the video denoising threshold, contrast enhancement threshold, sharpening processing threshold and resolution processing threshold corresponding to the video data under the first scoring value, and use the video denoising threshold, the contrast enhancement threshold, the sharpening processing threshold and the resolution processing threshold as the basis for starting the preset second optimization rule.

[0043] In this embodiment, in the machine learning stage, the basis for subsequent audio and video processing and how to process the audio and video will be determined based on multiple initial training audio and video. The initial training audio and video refers to multiple different audio and video data, or audio and video data after multiple processing of an audio and video data, such as performing different degrees of noise reduction, contrast, compilation and other processing on an audio and video data to obtain multiple audio and video data, and then the user's corresponding score for each audio and video data and the controller's own score for the audio and video data can be collected, and then the two score values ​​are used as the playback score. The two score values ​​can be collected based on different proportions to obtain the final playback score. At this time, it can be divided into a first score value for directly playing the audio and video, that is, a score value for direct playback without transmission. At this time, the impact of the data itself on the playback effect can be determined. Then, targeted processing is performed on the audio data and video data. In the case of audio data, the audio noise reduction threshold, audio equalization threshold and echo cancellation threshold corresponding to the audio data under the first scoring value will be determined, and the audio noise reduction threshold, the audio equalization threshold and the echo cancellation threshold will be used as the basis for starting the preset first optimization rule. At this time, the audio noise reduction threshold, audio equalization threshold and echo cancellation threshold corresponding to the audio data under the highest first scoring value will be determined, and then it will be determined whether it is necessary to start the preset first optimization rule to optimize the audio data. It is worth noting that the optimal audio noise reduction threshold, audio equalization threshold and echo cancellation threshold can also be determined for different audio noise reduction adjustments, audio equalization adjustments and echo cancellation adjustments respectively. That is, instead of directly determining the three thresholds under one scoring value, the three thresholds can also be determined under three targeted scores to ensure the accuracy of the threshold determination, and then ensure the accuracy of subsequent audio data processing. When it is video data, the video denoising threshold, contrast enhancement threshold, sharpening processing threshold and resolution processing threshold corresponding to the video data under the first scoring value will be determined, and then the above thresholds will be used as the basis for starting the preset second optimization rule. At this time, the video denoising threshold, contrast enhancement threshold, sharpening processing threshold and resolution processing threshold corresponding to the video data under the highest first scoring value will be determined, and then it will be determined whether it is necessary to start the preset second optimization rule to optimize the video data. At this time, the four thresholds can also be determined separately, which are not limited here, and then the audio and video data can be quality enhanced based on the thresholds to ensure the audio and video playback effect.

[0044] Furthermore, the playback score also includes a second score value played after transmission, and after the step of determining the playback score corresponding to the initial training audio and video, the following steps are included: Step S51, determining a first correspondence between the audio encoding method and the data feature of the audio data under the second scoring value, and using the first correspondence as an audio encoding transmission library in a preset machine learning database; Step S52, determining a second correspondence between the video encoding method and the data feature of the video data under the second scoring value, and using the second correspondence as a video encoding transmission library in a preset machine learning database.

[0045] In this embodiment, in addition to considering the impact of the data itself on the playback effect, the impact of transmission and encoding on audio and video playback will also be considered. At this time, the playback score also includes a second score value played after transmission, and then by determining the first correspondence between the audio encoding method and the data characteristics of the audio data under the second score value, and using the first correspondence as the audio encoding transmission library in the preset machine learning database, that is, determining the first correspondence between the audio encoding method and the data characteristics of the audio data under the highest second score value, it is possible to determine the audio encoding method and transmission method that uniquely correspond to the data characteristics of different audio data, and then select the encoding and transmission method in the subsequent selection to reduce the impact of encoding and transmission on audio playback. When it is video data, the second correspondence between the video encoding method and the data characteristics of the video data under the second score value will be determined, and the second correspondence will be used as the video encoding transmission library in the preset machine learning database, that is, determining the second correspondence between the video encoding method and the data characteristics of the video data under the highest second score value, it is possible to determine the video encoding method and transmission method that uniquely correspond to the data characteristics of different video data, and then select the encoding and transmission method in the subsequent selection to reduce the impact of encoding and transmission on video playback.

[0046] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the audio and video quality enhancement method based on machine learning in the present application. More forms of simple transformations based on this technical concept are all within the scope of protection of the present application.

[0047] This application also provides an audio and video quality enhancement device based on machine learning, please refer to Figure 4 , the audio and video quality enhancement device based on machine learning includes: The information acquisition module 10 is used to determine the audio and video data to be processed according to the initial audio and video information and preset machine learning parameters if the initial audio and video information to be processed is received; A type determination module 20, configured to determine audio and video processing data according to the audio and video data to be processed and a preset machine learning database, wherein the audio and video processing data includes audio processing data and video processing data; A first obstacle avoidance module 30, configured to determine audio quality enhancement data according to the audio processing data and a preset first optimization rule when the audio and video processing data is the audio processing data; The second obstacle avoidance module 40 is configured to determine video quality enhancement data according to the video processing data and a preset second optimization rule when the audio and video processing data is the video processing data.

[0048] The audio and video quality enhancement device based on machine learning provided by the present application adopts the audio and video quality enhancement method based on machine learning in the above-mentioned embodiment, which can solve the technical problem of poor audio and video playback effect. Compared with the prior art, the beneficial effects of the audio and video quality enhancement device based on machine learning provided by the present application are the same as the beneficial effects of the audio and video quality enhancement method based on machine learning provided by the above-mentioned embodiment, and other technical features in the audio and video quality enhancement device based on machine learning are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0049] The present application provides an audio and video quality enhancement device based on machine learning (which can be a part of a cleaning robot), and the audio and video quality enhancement device based on machine learning includes: at least one processor; and a memory connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the audio and video quality enhancement method based on machine learning in the above-mentioned embodiment 1. It is worth noting that the audio and video quality enhancement device based on machine learning can also contain other devices shared with the cleaning robot, such as a power supply, etc., which are not described one by one here.

[0050] Reference below Figure 5 , which shows a schematic diagram of the structure of a controller suitable for implementing the embodiment of the present application. The controller in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions: tablet computers), PMPs (Portable Media Players: portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5The controller shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0051] like Figure 5 As shown, the controller may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which may perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the controller are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following devices may be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the controller to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a controller with various devices, it should be understood that it is not required to implement or have all the devices shown. More or fewer devices can be implemented or have alternatively.

[0052] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0053] The controller provided by the present application adopts the audio and video quality enhancement method based on machine learning in the above embodiment, which can solve the technical problem of poor audio and video playback effect. Compared with the prior art, the beneficial effects of the controller provided by the present application are the same as the beneficial effects of the audio and video quality enhancement method based on machine learning provided in the above embodiment, and other technical features in the controller are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0054] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0055] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0056] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the audio and video quality enhancement method based on machine learning in the above-mentioned embodiment.

[0057] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor devices, devices or components, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution device, device or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0058] The computer-readable storage medium may be included in the controller; or may exist independently without being assembled into the controller.

[0059] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the controller, the controller: If the initial audio and video information to be processed is received, the audio and video data to be processed is determined according to the initial audio and video information and the preset machine learning parameters; Determining audio and video processing data according to the audio and video data to be processed and a preset machine learning database, wherein the audio and video processing data includes audio processing data and video processing data; When the audio and video processing data is the audio processing data, determining audio quality enhancement data according to the audio processing data and a preset first optimization rule; When the audio and video processing data is the video processing data, the video quality enhancement data is determined according to the video processing data and a preset second optimization rule.

[0060] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0061] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the device, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based device that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0062] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0063] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned audio and video quality enhancement method based on machine learning, and can solve the technical problem of poor audio and video playback effect. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the audio and video quality enhancement method based on machine learning provided in the above-mentioned embodiment, and will not be repeated here.

[0064] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned audio and video quality enhancement method based on machine learning.

[0065] The computer program product provided by this application can solve the technical problem of poor audio and video playback effect. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the audio and video quality enhancement method based on machine learning provided in the above embodiment, which will not be repeated here.

[0066] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for enhancing audio and video quality based on machine learning, characterized in that: The method for enhancing audio and video quality based on machine learning includes: If the initial audio and video information to be processed is received, the audio and video data to be processed is determined according to the initial audio and video information and the preset machine learning parameters; Determining audio and video processing data according to the audio and video data to be processed and a preset machine learning database, wherein the audio and video processing data includes audio processing data and video processing data; When the audio and video processing data is the audio processing data, determining audio quality enhancement data according to the audio processing data and a preset first optimization rule; When the audio and video processing data is the video processing data, the video quality enhancement data is determined according to the video processing data and a preset second optimization rule.

2. The method for enhancing audio and video quality based on machine learning as claimed in claim 1, characterized in that: The preset machine learning parameters include a standard video parameter threshold and a standard audio parameter threshold, and the step of determining the audio and video data to be processed according to the initial audio and video information and the preset machine learning parameters includes: When the initial audio and video information is audio information, detecting whether an audio parameter value in the audio information meets the standard audio parameter threshold, and when the audio parameter value in the audio information does not meet the standard audio parameter threshold, using audio data in the audio information as audio and video data to be processed; When the initial audio and video information is video information, it is detected whether the video parameter value in the video information meets the standard video parameter threshold. When the video parameter value in the video information does not meet the standard video parameter threshold, the video data in the video information is used as the audio and video data to be processed.

3. The method for enhancing audio and video quality based on machine learning as claimed in claim 1, characterized in that: The preset machine learning database includes an audio coding transmission library and a video coding transmission library, and the step of determining the audio and video processing data according to the audio and video data to be processed and the preset machine learning database includes: Determine audio data in the audio and video data to be processed, determine a first encoding and transmission mode corresponding to the data characteristics of the audio data in the audio encoding and transmission library, and bind the first encoding and transmission mode to the audio data as audio processing data; Determine the video data in the audio and video data to be processed, determine a second encoding and transmission method corresponding to the data characteristics of the video data in the video encoding and transmission library, and bind the second encoding and transmission method to the video data as video processing data.

4. The method for enhancing audio and video quality based on machine learning as claimed in claim 1, characterized in that: The preset first optimization rule includes an audio noise reduction rule, an audio equalization rule, and an echo cancellation rule. The step of determining the audio quality enhancement data according to the audio processing data and the preset first optimization rule includes: When the noise value of the audio processing data is greater than a preset audio noise reduction threshold, the audio processing data is denoised based on the audio noise reduction rule to obtain noise-reduced audio data, and when the equalization value of the audio processing data is greater than a preset audio equalization threshold, the noise-reduced audio data is audio-equalized based on the audio equalization rule to obtain equalized audio data; When the echo value of the audio processing data is greater than a preset echo cancellation threshold, performing echo cancellation on the equalized audio data based on the echo cancellation rule to obtain audio quality enhanced data; When the echo value of the audio processing data is less than or equal to a preset echo cancellation threshold, the equalized audio data is used as audio quality enhancement data.

5. The method for enhancing audio and video quality based on machine learning as claimed in claim 1, characterized in that: The preset second optimization rule includes a video denoising rule, a contrast enhancement rule, a sharpening processing rule and a resolution processing rule. The step of determining the video quality enhancement data according to the video processing data and the preset second optimization rule includes: When the noise value of the video processing data is greater than a preset video denoising threshold, denoising the video processing data based on the video denoising rule to obtain denoised video data; When the contrast of the video processing data is less than or equal to a preset contrast enhancement threshold, using the noise reduction video data as video quality enhancement data; When the contrast of the video processing data is greater than a preset contrast enhancement threshold, the noise reduction video data is contrast adjusted based on the contrast enhancement rule to obtain contrast adjustment data, and when the sharpening value of the contrast adjustment data is greater than a preset sharpening processing threshold and the resolution of the contrast adjustment data is greater than a preset resolution processing threshold, the contrast adjustment data is processed based on the sharpening processing rule and the resolution processing rule to obtain video quality enhanced data.

6. The method for enhancing audio and video quality based on machine learning according to any one of claims 1 to 5, characterized in that: The method for enhancing audio and video quality based on machine learning also includes: Acquire initial training audio and video, and determine a playback score corresponding to the initial training audio and video, wherein the playback score includes a first score value for direct playback; Determine an audio noise reduction threshold, an audio equalization threshold, and an echo cancellation threshold corresponding to the audio data under the first scoring value, and use the audio noise reduction threshold, the audio equalization threshold, and the echo cancellation threshold as a basis for starting a preset first optimization rule; Determine the video denoising threshold, contrast enhancement threshold, sharpening processing threshold and resolution processing threshold corresponding to the video data under the first scoring value, and use the video denoising threshold, the contrast enhancement threshold, the sharpening processing threshold and the resolution processing threshold as the basis for starting a preset second optimization rule.

7. The method for enhancing audio and video quality based on machine learning as claimed in claim 6, characterized in that: The playback score also includes a second score value played after transmission, and after the step of determining the playback score corresponding to the initial training audio and video, the following steps are included: Determine a first correspondence between the audio encoding method and the data feature of the audio data under the second scoring value, and use the first correspondence as an audio encoding transmission library in a preset machine learning database; A second corresponding relationship between the video encoding method and the data feature of the video data under the second scoring value is determined, and the second corresponding relationship is used as a video encoding transmission library in a preset machine learning database.

8. An audio and video quality enhancement device based on machine learning, characterized in that: The audio and video quality enhancement device based on machine learning includes: An information acquisition module, for determining the audio and video data to be processed according to the initial audio and video information and preset machine learning parameters if the initial audio and video information to be processed is received; A type determination module, used to determine audio and video processing data according to the audio and video data to be processed and a preset machine learning database, wherein the audio and video processing data includes audio processing data and video processing data; A first enhancement module, configured to determine audio quality enhancement data according to the audio processing data and a preset first optimization rule when the audio and video processing data is the audio processing data; The second enhancement module is used to determine video quality enhancement data according to the video processing data and a preset second optimization rule when the audio and video processing data is the video processing data.

9. An audio and video quality enhancement device based on machine learning, characterized in that: The machine learning-based audio and video quality enhancement device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the machine learning-based audio and video quality enhancement method as described in any one of claims 1 to 7.

10. A medium, characterized in that The medium is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the audio and video quality enhancement method based on machine learning are implemented as described in any one of claims 1 to 7.

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