CN116051477A - Image noise detection method and device for ultra-high definition video file - Google Patents
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Abstract
The embodiment of the invention provides an image noise detection method and device for an ultra-high definition video file. The method comprises the following steps: dividing a video file to be detected into a plurality of video clips to be detected; the plurality of video clips to be detected are decoded in parallel by utilizing a plurality of decoding modules, and decoded multi-frame video images are obtained; parallel noise detection is carried out on the decoded multi-frame video images by utilizing a plurality of image processing threads, so that respective noise detection results of the decoded multi-frame video images are obtained; and recording the position of the video image with image noise in the video file to be detected according to the respective noise detection results of the decoded multi-frame video images. By the method, image noise can be automatically detected on the video pictures in the video file, so that not only is labor liberated, but also the noise detection efficiency of the video file is improved, and time and labor are saved.
Description
Technical Field
The invention relates to the field of video quality detection, in particular to an image noise detection method and device for an ultra-high definition video file.
Background
Along with the improvement of human living standard and the rapid development of 4K ultra-high definition video technology, the development of 4K ultra-high definition television industry enters an acceleration stage, and a brand new visual experience is brought to vast television audience. In the whole manufacturing system of the 4K digital television, the shooting link has a great influence on the quality of the finally generated program picture.
Under the condition that the light of the ultra-high definition camera is darker, the imaging system can increase the exposure by adding a supplementary light source, image noise is commonly existed in the ultra-high definition materials shot under the condition, and if the materials are adopted as source materials to produce programs during post-production, the quality of the programs is greatly reduced.
Aiming at the phenomenon, the video material is required to be screened manually, and the human eyes are required to observe and distinguish the image noise of each frame of image in the video file, so that the time and the labor are consumed.
Disclosure of Invention
The embodiment of the invention provides an image noise detection method and device for an ultra-high definition video file, which are used for realizing automatic detection of the video file containing a noise picture.
An embodiment of the present invention provides a method for detecting image noise of an ultra-high definition video file, where the method includes:
Dividing a video file to be detected into a plurality of video clips to be detected;
the plurality of video clips to be detected are decoded in parallel by utilizing a plurality of decoding modules, and decoded multi-frame video images are obtained;
parallel noise detection is carried out on the decoded multi-frame video images by utilizing a plurality of image processing threads, so that respective noise detection results of the decoded multi-frame video images are obtained;
and recording the position of the video image with image noise in the video file to be detected according to the respective noise detection results of the decoded multi-frame video images.
Optionally, any image processing thread of the plurality of image processing threads performs noise detection on the decoded video image according to the following steps:
extracting the characteristics of the decoded video image of one frame to obtain the characteristic value of the decoded video image of one frame;
determining a target image area of which the mean square error of the pixel value is smaller than a mean square error threshold value from the decoded video image of one frame;
extracting the characteristics of the target image area to obtain the characteristic value of the target image area;
comparing the characteristic value of the target image area with the characteristic value of the decoded video image of one frame;
And under the condition that the difference value between the characteristic value of the target image area and the characteristic value of the decoded one-frame video image is larger than a difference value threshold value, determining that the noise detection result of the decoded one-frame video image is as follows: and image noise exists in the decoded video image of one frame.
Optionally, the determining, from the decoded video image of one frame, a target image area where a mean square error of pixel values is smaller than a mean square error threshold value includes:
sequentially carrying out graying, binarization and brightness calculation on the decoded video image of one frame to determine an image area with brightness smaller than a brightness threshold value from the decoded video image of one frame;
filtering the image area with the brightness smaller than the brightness threshold value to determine an image area with smooth texture from the image area with the brightness smaller than the brightness threshold value;
and calculating the mean square error of the pixel values of all pixel points in the image area with smooth texture so as to determine the target image area from the image area with smooth texture.
Optionally, the method further comprises:
generating an alarm message when a video image with image noise is detected for the first time;
Outputting the alarm message by using an alarm module, controlling the plurality of image processing threads to stop noise detection, and adding a noise mark for the video file to be detected;
and when the television program making request is detected, removing the video file with the noise mark from the video file library to obtain the video material for television program making.
Optionally, the method further comprises:
adding an image noise mark to the video image with image noise according to the respective noise detection results of the decoded multi-frame video image;
and when the playing operation of the video file to be detected is detected, adding prompt information for the video image with the image noise mark on a playing progress bar or in the video description.
A second aspect of an embodiment of the present invention provides an image noise detection apparatus for an ultra-high definition video file, the apparatus including:
the video segmentation module is used for dividing the video file to be detected into a plurality of video fragments to be detected;
the image frame acquisition module is used for decoding the plurality of video clips to be detected in parallel by utilizing the plurality of decoding modules to obtain decoded multi-frame video images;
the noise detection module is used for carrying out parallel noise detection on the decoded multi-frame video images by utilizing a plurality of image processing threads to obtain respective noise detection results of the decoded multi-frame video images;
And the noise recording module is used for recording the position of the video image with image noise in the video file to be detected according to the respective noise detection results of the decoded multi-frame video images.
Optionally, the noise detection module includes:
the first extraction module is used for extracting the characteristics of the decoded video image of one frame to obtain the characteristic value of the decoded video image of one frame;
the region determining module is used for determining a target image region of which the mean square error of the pixel value is smaller than a mean square error threshold value from the decoded video image of one frame;
the second extraction module is used for extracting the characteristics of the target image area to obtain the characteristic value of the target image area;
the comparison module is used for comparing the characteristic value of the target image area with the characteristic value of the decoded video image of one frame;
the noise detection sub-module is configured to determine, when a difference between the feature value of the target image area and the feature value of the decoded one-frame video image is greater than a difference threshold, that a noise detection result of the decoded one-frame video image is: and image noise exists in the decoded video image of one frame.
Optionally, the area determining module includes:
the first region determining submodule is used for sequentially carrying out graying, binarization and brightness calculation on the decoded one-frame video image so as to determine an image region with brightness smaller than a brightness threshold value from the decoded one-frame video image;
the second region determining submodule is used for carrying out filtering processing on the image region with the brightness smaller than the brightness threshold value so as to determine an image region with smooth texture from the image region with the brightness smaller than the brightness threshold value;
and the third region determination submodule is used for calculating the mean square error of the pixel values of all pixel points in the image region with smooth texture so as to determine the target image region from the image region with smooth texture.
Optionally, the apparatus further includes:
the alarm message generation module is used for generating an alarm message when the video image with the image noise is detected for the first time;
the first mark adding module is used for outputting the alarm message by utilizing the alarm module, controlling the plurality of image processing threads to stop noise detection, and adding a noise mark for the video file to be detected;
and the rejecting module is used for rejecting the video file with the noise mark from the video file library when the television program making request is detected, so as to obtain the video material for television program making.
Optionally, the apparatus further includes:
the second mark adding module is used for adding an image noise mark to the video image with the image noise according to the respective noise detection results of the decoded multi-frame video image;
and the information prompt module is used for adding prompt information for the video image with the image noise mark on a playing progress bar or in the video description when the playing operation for the video file to be detected is detected.
In the method for detecting the image noise of the ultra-high definition video file, firstly, the video file to be detected is divided into a plurality of video fragments to be detected; secondly, a plurality of decoding modules are utilized to decode a plurality of video clips to be detected in parallel, and decoded multi-frame video images are obtained; then, parallel noise detection is carried out on the decoded multi-frame video images by utilizing a plurality of image processing threads, so as to obtain respective noise detection results of the decoded multi-frame video images; and finally, recording the position of the video image with image noise in the video file to be detected according to the respective noise detection results of the decoded multi-frame video images. According to the image noise detection method for the ultra-high definition video file, the multi-thread parallel decoding can be carried out on the video file to be detected, the efficiency of obtaining each image frame in the video file to be detected is improved, and the parallel image noise detection can be carried out on decoded multi-frame video images through a plurality of image processing threads in the image processing module, so that the detection efficiency of the video file is improved on the basis of realizing the automatic noise detection of each frame of image in the video file to be detected, the manual work is liberated, time and labor are saved, and the technical problem that the image noise in the video material needs to be observed and distinguished by relying on the manual work at present is solved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments of the present invention will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of an image noise detection method for an ultra-high definition video file according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an embodiment of a method for detecting noise in an ultra-high definition video image;
fig. 3 is a block diagram of an image noise detecting device for an ultra-high definition video file according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, fig. 1 is a flowchart of an image noise detection method for an ultra-high definition video file according to an embodiment of the invention. The image noise detection method of the ultra-high definition video file provided by the embodiment can be applied to computer equipment, wherein the computer equipment can be a terminal, such as a smart phone, a tablet computer, a notebook computer, a desktop computer, an intelligent robot and the like; the computer device may also be a server, which may be a stand-alone server, a server cluster or a distributed system of multiple physical server projects, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network content delivery network), and basic cloud computing services such as big data and artificial intelligence platforms. As shown in fig. 1, the image noise detection method of the ultra high definition video file of the present embodiment may include the steps of:
step S11: and dividing the video file to be detected into a plurality of video clips to be detected.
In this embodiment, the video file to be detected may be determined first, and the video file to be detected may be segmented, and the video file to be detected may be divided into a plurality of video segments to be detected. The video file to be detected in this embodiment is a video file to be detected for image noise detection, and the video file to be detected may be an ultra-high definition video file or a video file with common image quality, which is not limited in this embodiment.
In this step, the video file to be detected may be divided into a plurality of video clips to be detected on average, or the video file to be detected may be divided into a plurality of video clips to be detected according to the importance degree of the video content and the association degree between the video content, which is not limited in this embodiment.
Step S12: and decoding the plurality of video clips to be detected in parallel by utilizing a plurality of decoding modules to obtain decoded multi-frame video images.
In this embodiment, the divided multiple video segments to be detected may be transmitted to multiple decoding modules, and the multiple video segments to be detected are decoded in parallel in a multithreading manner by using the multiple decoding modules, so as to obtain decoded multi-frame video images. In the step, in order to acquire each frame of video image in the video file to be detected, so as to facilitate the subsequent image noise detection for each frame of video image, the decoding efficiency of the video file can be improved by adopting multi-thread parallel decoding, and the acquisition time of the video image is saved.
Step S13: and performing parallel noise detection on the decoded multi-frame video images by using a plurality of image processing threads to obtain respective noise detection results of the decoded multi-frame video images.
In this embodiment, the image noise detection may be performed automatically on the decoded video image by the image processing module. The decoded multi-frame video image may be transmitted to an image processing module, and the image processing module of this embodiment has the capability of receiving multi-thread call at the same time, specifically, the multiple image processing threads of the image processing module may be utilized to perform parallel noise detection on the decoded multi-needle video image, so as to obtain respective noise detection results of the decoded multi-frame video image.
It should be noted that, in an embodiment, step S12 may be performed first and then step S13 may be performed, that is, after all video images in the video file to be detected are obtained, all video images are subjected to parallel noise detection through a plurality of image processing threads. In another embodiment, step S12 and step S13 may be performed synchronously, that is, while the video segment to be detected is decoded in parallel to obtain a subsequent multi-frame video image, the decoded multi-frame video image obtained previously may be transferred into multiple image processing threads to perform parallel noise detection, so that at the same time, multiple decoding modules perform multi-thread decoding, and multiple image processing threads also perform parallel noise detection, thereby maximizing the detection efficiency of the video file and further saving the detection time of the video file.
Step S14: and recording the position of the video image with image noise in the video file to be detected according to the respective noise detection results of the decoded multi-frame video images.
In this embodiment, after parallel noise detection is performed on the decoded multi-frame video image to obtain respective noise detection results of the decoded multi-frame video image, according to the respective noise detection results of the decoded multi-frame video image, a position of the video image with image noise recorded in the video file to be detected, where the video image with image noise appears in the video file to be detected, is recorded, so that recording of the image noise position of the video file to be detected is achieved.
For example, when the decoded multi-frame video image is obtained, each obtained frame of video image carries the frame bit number of the frame of video image in the file to be detected, so that after the respective noise detection results of the decoded multi-frame video image are obtained, the frame bit number of the video image with image noise in the file to be detected can be determined according to the respective noise detection results of the multi-frame video image.
According to the image noise detection method for the ultra-high definition video file, the decoding efficiency of the video file to be detected can be improved through multi-thread parallel decoding, parallel image noise detection can be simultaneously carried out through a plurality of image processing threads, and automatic image noise detection is carried out on each frame of image in the video file to be detected, so that the quality detection efficiency of the video file is improved, labor is relieved, and time and labor are saved.
In combination with the above embodiment, in an implementation manner, the embodiment of the present invention further provides an image noise detection method for an ultra-high definition video file. Specifically, in the method, for the above step S13, any one of the plurality of image processing threads performs noise detection on the decoded video image of one frame according to the following steps:
step S21: and extracting the characteristics of the decoded video image of one frame to obtain the characteristic value of the decoded video image of one frame.
In this embodiment, for a decoded video image of one frame, that is, for each decoded video image of one frame, feature extraction may be performed on the video image first to obtain a feature value of the video image.
Step S22: and determining a target image area of which the mean square error of the pixel value is smaller than a mean square error threshold value from the decoded video image of one frame.
In this step, the target image area may be determined from the decoded video image for one frame of the video image, that is, for each frame of the video image after decoding. The target image area is an area with strong image noise resistance in the video image, and the target image area is an area with pixel value mean square error smaller than a mean square error threshold value. The mean square error threshold is a mean square error value set in advance according to manual experience, and the specific numerical value of the mean square error threshold is not limited in any way in the embodiment.
Step S23: and extracting the characteristics of the target image area to obtain the characteristic value of the target image area.
In this embodiment, after the target image area of each frame of video image is obtained, feature extraction may be performed on the target image area of each frame of video image, to obtain a feature value of the target image area.
Step S24: and comparing the characteristic value of the target image area with the characteristic value of the decoded video image of one frame.
In this embodiment, after obtaining the feature value of the video image and the feature value of the target image area in the video image, the feature value of the target image area needs to be compared with the feature value of the video image.
Step S25: and under the condition that the difference value between the characteristic value of the target image area and the characteristic value of the decoded one-frame video image is larger than a difference value threshold value, determining that the noise detection result of the decoded one-frame video image is as follows: and image noise exists in the decoded video image of one frame.
In this embodiment, after comparing the feature value of the target image area with the feature value of the video image to obtain a difference value between the feature value of the target image area and the feature value of the video image, the difference value may be referred to as an absolute value, and the difference value may be compared with a difference threshold. The difference threshold is a maximum value of difference representing that no image noise occurs, which is set in advance according to manual experience, and the specific value of the difference threshold is not limited in any way in this embodiment.
Under the condition that the difference value between the characteristic value of the target image area and the characteristic value of the video image is larger than a difference value threshold value, determining that image noise exists in the video image, and determining that the noise detection result of the video image is: image noise is present in the video image.
In this embodiment, for noise detection of each frame of video image, a target image area with strong noise resistance in the video image is determined first, then, by comparing a feature value of the whole video image with a feature value of the target image area, when a difference value between the feature value and the feature value is greater than a preset maximum value representing that no image noise occurs, it is determined that the video image has image noise. By the method, the accuracy of image noise detection is greatly improved, manual experience is not needed, and the problem of inaccurate image noise detection caused by insufficient manual experience is avoided.
In another embodiment, there may be a difference range having upper and lower limits, which is also set by human experience, and the present embodiment is not particularly limited thereto, and the difference of the feature values may represent the presence of image noise when the difference falls within the difference range. That is, in the case where the difference between the feature value of the target image area and the feature value of the decoded one-frame video image falls within the difference range, the noise detection result of the decoded one-frame video image is determined as: and image noise exists in the decoded video image of one frame.
In addition, in an embodiment, the image processing thread may compare the feature value of the target image area with the feature value of the decoded video image, obtain a difference value between the feature value of the target image area and the feature value of the decoded video image, and then transmit the feature difference value to the alarm module, where a difference threshold value or a difference range is set in advance in the alarm module, and the alarm module compares the feature difference value with the difference threshold value or the difference range, so that the alarm module determines whether there is image noise in the video image, and records a position where the image noise appears in the video.
In combination with the above embodiment, in an implementation manner, the embodiment of the present invention further provides an image noise detection method for an ultra-high definition video file. In the method, the step S22 may include steps S31 to S33:
step S31: and sequentially carrying out graying, binarization and brightness calculation on the decoded video image of one frame so as to determine an image area with brightness smaller than a brightness threshold value from the decoded video image of one frame.
In this embodiment, for a decoded video image of one frame, that is, for each decoded video image of one frame, the image processing thread may sequentially perform image preprocessing of graying and binarizing on the video image to obtain a preprocessed video image, and then perform pixel brightness calculation on the preprocessed video image to obtain a brightness value of each pixel point in the video image.
In this embodiment, a luminance threshold value is preset, and the luminance threshold value may be set according to manual experience, and the specific value of the luminance threshold value is not particularly limited. After obtaining the brightness value of each pixel point in the video image, determining an image area with brightness less than a brightness threshold value from the video image, wherein the image area with brightness less than the brightness threshold value is: an image area in the video image is composed of pixels with brightness values less than a brightness threshold.
Step S32: and filtering the image area with the brightness smaller than the brightness threshold value to determine the image area with smooth texture from the image area with the brightness smaller than the brightness threshold value.
In this embodiment, after determining an image area with brightness less than the brightness threshold, a filter may be used to perform filtering processing on the image area with brightness less than the brightness threshold, so as to determine an image area with smooth texture, where the image area with smooth texture refers to: and the difference between the brightness values of the pixel points and the surrounding pixel points is lower than the brightness difference threshold value. Similarly, in this embodiment, a luminance difference threshold is preset, and the luminance difference threshold may be set according to manual experience, and the specific value of the luminance difference threshold is not particularly limited.
Step S33: and calculating the mean square error of the pixel values of all pixel points in the image area with smooth texture so as to determine the target image area from the image area with smooth texture.
In this embodiment, after determining the image area with smooth texture, the mean square error calculation may be performed for each pixel point in the image area with smooth texture, and the target image area is determined from the image area with smooth texture, that is, the target image area formed by the pixel points with mean square error smaller than the mean square error threshold is determined from the image area with smooth texture.
In this embodiment, considering that the imaging system may increase the exposure by adding a supplemental light source in a dark light situation, image noise is common to video files generated in this situation, such as white noise, gaussian noise, impulse noise, signal-dependent noise, etc. generated for ultra-high definition cameras, and the common point of these noise is that they occur in a scene where the camera is not sufficiently light-entering. Therefore, in order to cope with these noise types, the present embodiment adopts a targeted detection method for the picture noise that is commonly generated by the ultra-high-definition camera in the case of insufficient light intake: in the method, firstly, a picture with darker brightness is found in a video image, so that a picture with smooth texture is found in the picture with darker brightness, then, a target image area with small signal mean square error is found in a picture part with smooth texture, namely, a target image area with strong noise resistance is determined from an image part with most noise occurrence possibility, so that the characteristic value of the area and the characteristic value of the whole image are compared, the obtained characteristic difference value is compared with a threshold value, and whether the video image has image noise can be accurately determined.
In combination with the above embodiment, in an implementation manner, the embodiment of the present invention further provides an image noise detection method for an ultra-high definition video file. In addition to the above steps, the method may further include step S41 to step S43:
step S41: an alarm message is generated when a video image with image noise is detected for the first time.
In this embodiment, when noise detection is performed on each frame of video image in the video to be detected, an alarm message may be generated when a video image with image noise is detected for the first time.
Step S42: and outputting the alarm message by using an alarm module, controlling the plurality of image processing threads to stop noise detection, and adding a noise mark for the video file to be detected.
In this embodiment, after the alarm message is obtained, the alarm module may be used to output the alarm message and stop noise detection for the video file to be detected, that is, control multiple image processing threads in the image processing module to stop noise detection for multiple frames of video images in the video file to be detected, and add a noise mark to the video file to be detected.
Step S43: and when the television program making request is detected, removing the video file with the noise mark from the video file library to obtain the video material for television program making.
In this embodiment, when a request for producing a television program is detected, when selecting a video material, a video file with a noise mark may be removed from a video file library according to the noise mark, so as to obtain a video material produced by a television program that does not include the noise mark.
In this embodiment, when noise detection is performed on a video file to be detected, when a video image with image noise is detected for the first time, detection can be stopped, and no noise mark is added to the video file to be detected, so that when video materials are selected for television program production, the video file with the noise mark is automatically removed, thereby meeting the high quality requirement of television program production, and avoiding manual screening of the video materials.
With reference to any of the above embodiments, in an implementation manner, the embodiment of the present invention further provides an image noise detection method for an ultra-high definition video file. In the method, in addition to the above steps, step S51 and step S52 may be included:
step S51: and adding an image noise mark to the video image with image noise according to the respective noise detection results of the decoded multi-frame video image.
In this embodiment, after obtaining the respective noise detection results of the decoded multi-frame video images, an image noise flag may be added to the noise detection result for the video image that is in image noise according to the respective noise detection results of the decoded multi-frame video images.
Step S52: and when the playing operation of the video file to be detected is detected, adding prompt information for the video image with the image noise mark on a playing progress bar or in the video description.
In this embodiment, the image noise marks are added to the video images detected in the video file to be detected, so that when the play operation for the video file to be detected is detected, the prompt information for the video image with the image noise marks can be added to the play progress bar or the video description.
In this embodiment, when noise detection is performed on a video file to be detected, an image noise mark is added to all video images with image noise, so that the positions where all noise appears in the video can be recorded in a video file detection report later, or when a play operation for the video file to be detected is detected, a prompt message for the video images with image noise marks can be added to a play progress bar or a video description to prompt a user for the positions of image noise in the video, so that targeted processing, such as denoising processing, deleting processing, and the like, can be performed on the video images with image noise.
Referring to fig. 2, fig. 2 is a schematic diagram illustrating an ultra-high definition video image noise detection method according to an embodiment of the invention. As shown in fig. 2, a targeted detection method is provided for the image noise commonly generated by the ultra-high definition camera under the condition of insufficient light intake:
firstly, segmenting an ultra-high definition file to be detected to obtain a plurality of video segments to be detected, and transmitting the video segments to be detected into a plurality of decoding modules to perform multi-thread parallel decoding.
And secondly, transmitting each decoded frame of image into an image processing module, wherein the image processing module has the capacity of simultaneously receiving multi-thread call, thereby realizing multi-thread image processing of video images.
Then, in the image processing module, each image processing thread can sequentially perform graying, binarization and pixel brightness calculation on each incoming frame of image to find an image area with darker brightness, then find an image area with smooth texture in the image area with darker brightness through filtering treatment, and find a target image area with small mean square error from the image area with smooth texture, so that the characteristic value of the whole image and the characteristic value of the target image area are compared, and a characteristic value comparison result is obtained.
And then, the characteristic value comparison result is transmitted into an alarm module, the alarm module compares the characteristic value comparison result with a preset threshold value, if the characteristic value comparison result exceeds the difference value threshold value or falls within a difference value range, the image is judged to have noise, and the alarm module records the picture position where the noise occurs, namely, the number of frames in the video file, in which the image frame with the image noise exists.
And finally, the alarm module writes the detection result into a detection report, and finally the positions of all noise in the video are recorded in the detection report, so that the automatic detection and screening of the video materials containing the noise picture are realized.
It should be noted that, for simplicity of description, the method embodiments are shown as a series of acts, but it should be understood by those skilled in the art that the embodiments are not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the embodiments. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred embodiments, and that the acts are not necessarily required by the embodiments of the invention.
Based on the same inventive concept, an embodiment of the present invention provides an image noise detection apparatus 300 for an ultra-high definition video file, and the image noise detection apparatus 300 for an ultra-high definition video file is applicable to a computer device. Referring to fig. 3, fig. 3 is a block diagram illustrating an image noise detecting apparatus for an ultra-high definition video file according to an embodiment of the present invention. As shown in fig. 3, the image noise detecting apparatus 300 of the ultra high definition video file includes:
the video segmentation module 301 is configured to divide a video file to be detected into a plurality of video segments to be detected;
the image frame obtaining module 302 is configured to decode the multiple video segments to be detected in parallel by using multiple decoding modules, so as to obtain decoded multi-frame video images;
the noise detection module 303 is configured to perform parallel noise detection on the decoded multiple frames of video images by using multiple image processing threads, so as to obtain respective noise detection results of the decoded multiple frames of video images;
the noise recording module 304 is configured to record, according to respective noise detection results of the decoded multi-frame video images, a position of the video image with the image noise in the video file to be detected.
Optionally, the noise detection module 303 includes:
The first extraction module is used for extracting the characteristics of the decoded video image of one frame to obtain the characteristic value of the decoded video image of one frame;
the region determining module is used for determining a target image region of which the mean square error of the pixel value is smaller than a mean square error threshold value from the decoded video image of one frame;
the second extraction module is used for extracting the characteristics of the target image area to obtain the characteristic value of the target image area;
the comparison module is used for comparing the characteristic value of the target image area with the characteristic value of the decoded video image of one frame;
the noise detection sub-module is configured to determine, when a difference between the feature value of the target image area and the feature value of the decoded one-frame video image is greater than a difference threshold, that a noise detection result of the decoded one-frame video image is: and image noise exists in the decoded video image of one frame.
Optionally, the area determining module includes:
the first region determining submodule is used for sequentially carrying out graying, binarization and brightness calculation on the decoded one-frame video image so as to determine an image region with brightness smaller than a brightness threshold value from the decoded one-frame video image;
The second region determining submodule is used for carrying out filtering processing on the image region with the brightness smaller than the brightness threshold value so as to determine an image region with smooth texture from the image region with the brightness smaller than the brightness threshold value;
and the third region determination submodule is used for calculating the mean square error of the pixel values of all pixel points in the image region with smooth texture so as to determine the target image region from the image region with smooth texture.
Optionally, the apparatus 300 further includes:
the alarm message generation module is used for generating an alarm message when the video image with the image noise is detected for the first time;
the first mark adding module is used for outputting the alarm message by utilizing the alarm module, controlling the plurality of image processing threads to stop noise detection, and adding a noise mark for the video file to be detected;
and the rejecting module is used for rejecting the video file with the noise mark from the video file library when the television program making request is detected, so as to obtain the video material for television program making.
Optionally, the apparatus 300 further includes:
the second mark adding module is used for adding an image noise mark to the video image with the image noise according to the respective noise detection results of the decoded multi-frame video image;
And the information prompt module is used for adding prompt information for the video image with the image noise mark on a playing progress bar or in the video description when the playing operation for the video file to be detected is detected.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described by differences from other embodiments, and identical and similar parts between the embodiments are all enough to be referred to each other.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The embodiments of the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those having ordinary skill in the art without departing from the spirit of the present invention and the scope of the claims, which are to be protected by the present invention.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, etc.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
1. An image noise detection method for an ultra-high definition video file, the method comprising:
dividing a video file to be detected into a plurality of video clips to be detected;
the plurality of video clips to be detected are decoded in parallel by utilizing a plurality of decoding modules, and decoded multi-frame video images are obtained;
parallel noise detection is carried out on the decoded multi-frame video images by utilizing a plurality of image processing threads, so that respective noise detection results of the decoded multi-frame video images are obtained;
and recording the position of the video image with image noise in the video file to be detected according to the respective noise detection results of the decoded multi-frame video images.
2. The method for detecting image noise of an ultra-high definition video file according to claim 1, wherein any one of the plurality of image processing threads performs noise detection on a decoded video image according to the steps of:
extracting the characteristics of the decoded video image of one frame to obtain the characteristic value of the decoded video image of one frame;
determining a target image area of which the mean square error of the pixel value is smaller than a mean square error threshold value from the decoded video image of one frame;
Extracting the characteristics of the target image area to obtain the characteristic value of the target image area;
comparing the characteristic value of the target image area with the characteristic value of the decoded video image of one frame;
and under the condition that the difference value between the characteristic value of the target image area and the characteristic value of the decoded one-frame video image is larger than a difference value threshold value, determining that the noise detection result of the decoded one-frame video image is as follows: and image noise exists in the decoded video image of one frame.
3. The method for detecting image noise of an ultra-high definition video file according to claim 2, wherein determining a target image area with a mean square error of pixel values smaller than a mean square error threshold value from the decoded video image frame comprises:
sequentially carrying out graying, binarization and brightness calculation on the decoded video image of one frame to determine an image area with brightness smaller than a brightness threshold value from the decoded video image of one frame;
filtering the image area with the brightness smaller than the brightness threshold value to determine an image area with smooth texture from the image area with the brightness smaller than the brightness threshold value;
And calculating the mean square error of the pixel values of all pixel points in the image area with smooth texture so as to determine the target image area from the image area with smooth texture.
4. A method for detecting image noise of an ultra high definition video file according to any one of claims 1 to 3, further comprising:
generating an alarm message when a video image with image noise is detected for the first time;
outputting the alarm message by using an alarm module, controlling the plurality of image processing threads to stop noise detection, and adding a noise mark for the video file to be detected;
and when the television program making request is detected, removing the video file with the noise mark from the video file library to obtain the video material for television program making.
5. A method for detecting image noise of an ultra high definition video file according to any one of claims 1 to 3, further comprising:
adding an image noise mark to the video image with image noise according to the respective noise detection results of the decoded multi-frame video image;
and when the playing operation of the video file to be detected is detected, adding prompt information for the video image with the image noise mark on a playing progress bar or in the video description.
6. An image noise detection apparatus for an ultra-high definition video file, the apparatus comprising:
the video segmentation module is used for dividing the video file to be detected into a plurality of video fragments to be detected;
the image frame acquisition module is used for decoding the plurality of video clips to be detected in parallel by utilizing the plurality of decoding modules to obtain decoded multi-frame video images;
the noise detection module is used for carrying out parallel noise detection on the decoded multi-frame video images by utilizing a plurality of image processing threads to obtain respective noise detection results of the decoded multi-frame video images;
and the noise recording module is used for recording the position of the video image with image noise in the video file to be detected according to the respective noise detection results of the decoded multi-frame video images.
7. The image noise detection apparatus of an ultra high definition video file according to claim 6, wherein said noise detection module comprises:
the first extraction module is used for extracting the characteristics of the decoded video image of one frame to obtain the characteristic value of the decoded video image of one frame;
the region determining module is used for determining a target image region of which the mean square error of the pixel value is smaller than a mean square error threshold value from the decoded video image of one frame;
The second extraction module is used for extracting the characteristics of the target image area to obtain the characteristic value of the target image area;
the comparison module is used for comparing the characteristic value of the target image area with the characteristic value of the decoded video image of one frame;
the noise detection sub-module is configured to determine, when a difference between the feature value of the target image area and the feature value of the decoded one-frame video image is greater than a difference threshold, that a noise detection result of the decoded one-frame video image is: and image noise exists in the decoded video image of one frame.
8. The image noise detection apparatus of an ultra high definition video file according to claim 7, wherein said region determination module comprises:
the first region determining submodule is used for sequentially carrying out graying, binarization and brightness calculation on the decoded one-frame video image so as to determine an image region with brightness smaller than a brightness threshold value from the decoded one-frame video image;
the second region determining submodule is used for carrying out filtering processing on the image region with the brightness smaller than the brightness threshold value so as to determine an image region with smooth texture from the image region with the brightness smaller than the brightness threshold value;
And the third region determination submodule is used for calculating the mean square error of the pixel values of all pixel points in the image region with smooth texture so as to determine the target image region from the image region with smooth texture.
9. The apparatus for detecting image noise of an ultra high definition video file according to any one of claims 6 to 8, further comprising:
the alarm message generation module is used for generating an alarm message when the video image with the image noise is detected for the first time;
the first mark adding module is used for outputting the alarm message by utilizing the alarm module, controlling the plurality of image processing threads to stop noise detection, and adding a noise mark for the video file to be detected;
and the rejecting module is used for rejecting the video file with the noise mark from the video file library when the television program making request is detected, so as to obtain the video material for television program making.
10. The apparatus for detecting image noise of an ultra high definition video file according to any one of claims 6 to 8, further comprising:
the second mark adding module is used for adding an image noise mark to the video image with the image noise according to the respective noise detection results of the decoded multi-frame video image;
And the information prompt module is used for adding prompt information for the video image with the image noise mark on a playing progress bar or in the video description when the playing operation for the video file to be detected is detected.
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