WO2018058090A1 - Procédé d'évaluation de qualité d'image sans référence - Google Patents

Procédé d'évaluation de qualité d'image sans référence Download PDF

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Publication number
WO2018058090A1
WO2018058090A1 PCT/US2017/053393 US2017053393W WO2018058090A1 WO 2018058090 A1 WO2018058090 A1 WO 2018058090A1 US 2017053393 W US2017053393 W US 2017053393W WO 2018058090 A1 WO2018058090 A1 WO 2018058090A1
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image
electronic image
computing
values
data structure
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PCT/US2017/053393
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English (en)
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Dapeng Oliver Wu
Ruigang FANG
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University Of Florida Research Foundation Incorporated
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30168—Image quality inspection

Definitions

  • the present disclosure is generally related to the field of image quality assessment for electronic images processed in computing devices.
  • both video and image media comprising electronic images have become a widely popular form of internet traffic and are displayed on a variety of electronic devices to media consumers.
  • a media consumer's experience during the consumption of video/image media can be negatively impacted by distortions induced by compression and/or transmission losses over the internet or other computer networks.
  • the media consumer's experience of viewing video/image media may also be impacted by characteristics of the electronic device displaying the media to the consumer, such as display pixel resolution and range of color gamut.
  • a source image is available, or other image is available as a reference of what the electronic image is supposed to look like without the effects of compression, transmission or other processes that impact image quality
  • an automated assessment of image quality for the electronic image might be made by comparing the electronic image with the reference image.
  • a no-reference image quality assessment may be performed to assess the quality of the electronic image.
  • no-reference image quality assessment techniques have not adequately reflected the image quality as perceived by a user.
  • a computer-implemented method for assessing quality of an electronic image comprises computing values for a plurality of attributes of the electronic image; selecting a plurality of entries from a computer data structure. Each entry of the data structure comprises values of the plurality of attributes and a score. The selecting comprises selecting based on a vector difference of the values of the plurality of attributes of the electronic image relative to the values of the plurality of attributes of entries in the data structure. The method further comprises computing a quality score for the electronic image as a combination of the scores of the selected entries from the data structure.
  • a non-transitory computer readable medium comprising computer readable instructions that when executed by a processor, cause the processor to perform a method.
  • the method comprises the acts of computing values for a plurality of attributes of the electronic image; selecting a plurality of entries from a computer data structure. Each entry of the data structure comprises values of the plurality of attributes and a score.
  • the selecting comprises selecting based on a vector difference of the values of the plurality of attributes of the electronic image relative to the values of the plurality of attributes of entries in the data structure.
  • the method further comprises computing a quality score for the electronic image as a combination of the scores of the selected entries from the data structure.
  • FIG. 1A is an image
  • FIG. IB is a data plot illustrating the Laplace distribution of the image of FIG. 1A;
  • FIG. 2A is an image
  • FIGs. 2B, 2C are data plots illustrating two sub-image Laplace distributions of the image of FIG. 2A;
  • FIG. 3A is an image
  • FIGs. 3B are data plots illustrating the relationship between pixel distance and the variance of the Laplace distribution for the image of FIG. 3A;
  • FIG. 4A shows an illustrative image with a 3x3 window with variable names denoting the image intensity at the corresponding positions;
  • FIG. 4B shows a data plot illustrating Laplace distribution showing a threshold and the probability of being over the threshold;
  • FIGs. 5A-C show data plots illustrating the relationship between blurriness and three features
  • FIG. 6 shows a schematic diagram illustrating the relationship between frame sampling structure for vertical direction
  • FIG. 7 shows a data plot illustrating the relationship between blockiness feature value and blockiness percentage
  • FIGs. 8A-C are data plots illustrating the comparison between clear and distorted images
  • FIGs 9A-D are exemplary images with Gaussian blur
  • FIGs 10A-D are exemplary images with white noise
  • FIGs. 11A-D are exemplary images with block artifacts
  • FIG. 12 shows a data plot illustrating BNB performance for LIVE image database
  • FIG. 13 is a schematic diagram of an illustrative computer 5000 on which any aspect of the present disclosure may be implemented.
  • Quality scores as computed herein may accurately reflect human perception of the quality of an electronic image. As these techniques can be performed without a reference image, they may be applied in settings in which arbitrary images are processed, including many settings in which images are transmitted for storage or display in a computer network, such as the Internet.
  • the image quality scores may be used, for example, to automatically select parameters of image processing or display in a way that reduces computer resources without unacceptably degrading image quality as perceived by a human viewer of the images.
  • a plurality of image quality attributes for a given image can be quantified to create a vector of metrics for the attributes.
  • the vector of metrics may be used to select entries from a data structure storing multiple examples of those metrics linked to human perception scores.
  • the selected entries may be used to compute a quality score representing a human perception of quality of the given image.
  • the entries in the data set may be precomputed based on a set of images, such as a known library of images used in image processing research, but do not have to represent the same scene as the electronic image being processed. Accordingly, the quality score may be computed without the use of a reference image for the electronic image being processed.
  • the process of deriving an image quality score may be applied in settings in which automated processing is desirable.
  • the data structure may act as a "codebook" for processing other images, which are potentially unrelated to the images used to create the codebook.
  • the codebook may be constructed using a library of images with known human perception scores.
  • the codebook may be a data structure with a plurality of entries, each entry comprising a vector of metrics for a plurality of image quality attributes as well as a human perception score assigned to a sample image.
  • the codebook may be constructed using a library of popular images as sample images. Each entry in the codebook comprises a vector of metrics for a sample image in the library and a human perception score obtained by rating the sample image with real humans.
  • each entry in the codebook need not correspond to any single sample image.
  • each entry in the codebook may reflect an average of multiple sample images that were distorted to produce the same metrics of image quality.
  • the codebook may be created heuristically based on observations of human sensitivity to variations in the metrics.
  • the codebook may be constructed to reflect conditions under which the image is to be presented to a human viewer.
  • the human perception score for each sample image in the library may correspond substantially to the sample image displayed on a particular class of electronic devices.
  • the codebook may be constructed by rating a library of images on smartphone screens, high resolution flat-panel TVs, or computer monitors to reflect human perception scores when viewed on the respective class of devices, and different codebooks may be used in different automated systems, depending on the intended display format or, in some embodiments, a codebook may be selected automatically based on hardware or other configuration information obtained from a device to display an image.
  • a vector of metrics for a plurality of attributes is computed for the electronic image and compared with the vectors for entries in the codebook. Entries from the codebook may be selected for further processing based on similarity between the vector for the reference image and vectors for entries in the codebook.
  • a vector distance is computed between the vector of the electronic image the vector of entries in the codebook.
  • a number of "nearest neighbor" entries from the codebook with vector distances smaller than a certain threshold may be selected as they represent a group of sample images most similar to the electronic image.
  • the human perception scores of the selected codebook entries may then be used to synthesize a human perception score for the electronic image that most represent how a human user would perceive the quality of the electronic image.
  • the human perception score for the electronic image may be computed by a weighted average of human perception scores of the selected codebook entries.
  • a higher weight is assigned to a codebook entry with a smaller vector distance to the electronic image, such that more similar sample images are given more weight in the comparison.
  • the vector distance may be computed in any suitable fashion, such as an
  • the vector distance is a weighted Euclidean distance.
  • any image quality attribute may be used in assessing image quality according to aspects of the present application.
  • quantitative measurements for specific artifacts that affect image quality may be used.
  • the values for each of blurriness, noisiness and blockiness (hereinafter also referred to as "BNB”) measurements may be used to form the vector of metrics to assess the quality of a given electronic image using a codebook of vectors of BNB metrics and human perception scores of known sample images, according to the sections discussed above.
  • the BNB metrics quantifies the blurriness, noisiness and blockiness of a given image, which are considered three critical factors affecting users' quality of experience (QoE).
  • a metric for each BNB artifact features for each type of artifact are first extracted from the changing Laplace distribution, and then the quantitative relationship between the feature value and the variation of the artifact is identified. This method is rooted in the observation that for any image the difference between any two adjacent pixel values follows a generalized Laplace distribution with zero mean. This Laplace distribution changes differently when the image experiences various types of artifacts such as BNB.
  • a k-Nearest Neighbors algorithm (k-NN) is used to map a vector of three BNB metrics of an electronic image to a human perception score.
  • the computation of BNB metrics and the k-NN approach require less computation as compared with more complex no-reference image quality assessment methods and may lead to cost savings in terms of reduced requirement of processor computing resources.
  • the human perception score may be used to indicate the quality of a given image displayed on an electronic device as perceived by an end user of the electronic device.
  • the given image may be part of media transmitted via teh internet for consumption by the end user, such as a still image of a video.
  • the no- reference human perception score assessed from the image may be used in real time by the media supplier to gauge the quality of media delivered to the end user. For example a media supplier may adjust encoding parameters and/or increase transmission data rate if image quality is poor. In another example, a media supplier may determine that the image quality as perceived by the end-user on a portable electronic device with a low resolution display is sufficiently high and proceed to reduce media transmission data rate by e.g. down sampling to a lower resolution to save related bandwidth and storage costs without affecting the user's experience.
  • quality scores as described herein may be computed by a sending computing device preparing information for transmission to a receiving device.
  • the sending device may use a codebook selected based on information about the display conditions sent by the receiving device to compute the quality scores.
  • an image quality score may be computed by the receiving device, and transmitted to the sending device for use by the sending device in selecting parameters of the images to be sent.
  • the receiving device may select the image parameters from the quality score and send these parameters instead of or in addition to the quality score or may use the quality scores to otherwise control the display of images.
  • the techniques introduced above and discussed in greater detail below may be implemented in any of numerous ways, as the techniques are not limited to any particular manner of implementation. Examples of details of implementation are provided herein solely for illustrative purposes.
  • the techniques disclosed herein may be used individually or in any suitable combination, as aspects of the present disclosure are not limited to the use of any particular technique or combination of techniques.
  • Decoded frame assessment offers three different methods to judge the image quality based on the availability of the original image: full-reference (FR), reduced- reference (RR), and no-reference (NR).
  • FR methods have access to the original image, which may provide a means to offer certain connections to human visual perception using mean squared error (MSE), peak signal to noise ratio (PSNR), or structural similarity index (SSIM)[17].
  • MSE mean squared error
  • PSNR peak signal to noise ratio
  • SSIM structural similarity index
  • RRED Reduced reference entropic differencing
  • NR handle the instances where information regarding the original image is unavailable. With the rise of scenarios that do not offer a mechanism for access to information from the original video/image, no-reference image quality assessment is both an essential and urgently needed technique.
  • AS methods are based on the assumption that distortions of an image are caused by specific artifacts; hence it is straightforward to take a divide-and-conquer approach, i.e., modeling the effect of each individual artifact on image quality and combining the effects of individual artifacts into a single image quality score (the key idea behind all AS methods). Therefore, the advantage of AS methods lies in directly identifying and quantifying the physical causes of image distortion. However, existing AS methods are not able to characterize the complicated interactions among multiple artifacts. These AS algorithms may perform well if a test image experiences only one type of artifact, but in reality an image may experience a mixture of multiple artifacts.
  • NAS methods are inherently independent of specific types of artifacts since they derive features from different kinds of transformed domains, such as Wavelet[26], DCT [16], Spatial[l l], Curvelet [7], and Gradient [24], which are all non- artifact- specific. In most cases the features are entropies, or statistic parameters of the transformed coefficients.
  • NAS methods utilize more complex projection techniques when transforming from feature vectors to quality scores, such as
  • SVM Support Vector Regression
  • NNR Neural Network Regression
  • IQA image quality assessment
  • NAS methods have recently become the forefront of image quality assessment.
  • the BIQI method was proposed, which is a two-step no-reference image quality assessment framework. Given a distorted image, the first step performs the wavelet transform and extracts features for estimation of the presence of a set of distortions which include those introduced by JPEG, JPEG2000, white noise, Gaussian blur, and fast fading. The probability of each distortion in the image is then estimated.
  • This first step is considered a classification step.
  • the second step evaluates the quality of the image across each of these distortions by applying support vector regression on the wavelet coefficients.
  • BIQI considers image distortion, the features it uses are derived from NSS.
  • SSEQ [8], CurveletQA [7], and DIIVINE [14] also utilize the same type of two-step framework as described in BIQI, however the features used for each are from the spectral entropy and local spatial domain, curvelet domain, and wavelet domain, respectively.
  • a method proposed in [11], BRISQUE derives features from the empirical distribution of locally normalized luminance values and their products under a spatial natural scene statistic model. These features are then used in support vector regression to map image features to an image quality score.
  • BRISQUE belongs to a one-step framework which does not require distortion classification.
  • Other methods such as those in both [16] and [24] are similar to BRISQUE in that regard.
  • the major difference between these 3 one-step methods is the feature space.
  • the authors extract features from the Discrete
  • a NR scheme which combines the best features of both the AS and NAS methods. This is accomplished by developing three artifact- specific metrics and nonlinearly combining them.
  • ⁇ the effect of a single type of artifact (i.e., blockiness, blurriness, or noisiness) on the Laplace distribution is similar and independent of image content.
  • BNB metrics blockiness, noisiness, and blurriness
  • HVS human visual system
  • Section 2 of this disclosure explores several key properties of the Laplace distribution for natural scene images, which are the basis for the design of our method.
  • Section 3 we describe the three BNB metrics.
  • Section 4 we verify our metrics using two aspects of experimentation and provide detailed results. The algorithm developed and used to perform supervised learning to predict the perceptual value is discussed in Section 5.
  • Section 7 concludes this paper with a discussion of final remarks.
  • Property 1 For a difference set, D, of any natural scene image, the statistical properties of any two equally down-sampled sub-sets, D 1 and D 2 , have the same statistical properties.
  • FIG. 2 displays an experimental result showing the original image in FIG. 2A and the Laplace distributions of set / 0 and set / t having variances 130.56 and 131.05, respectively in FIG. 2B.
  • Property 2 For any two pixels of a natural scene image, the difference in pixel values follows a Laplace distribution that is related to the spatial distance between the pixels; an increased distance corresponds to a larger variance in the Laplace distribution.
  • FIG. 3B visualizes the Laplace distributions of some such increases in the spatial distances between pixels, d .
  • Property 3 After convolving a natural scene image with a low-pass filter, f x , the difference of values of the same pixel in the original image and the processed image will also follow a Laplace distribution.
  • f x as a simple low-pass filter which can both lessen Gaussian noise and blur an image. Processing an image using this filter causes the variance of the difference of two adjacent pixel values to decrease.
  • P a threshold in the distribution and define P as the probability of the difference of adjacent pixel values being larger than the threshold
  • P becomes smaller when the image is convolved with f x .
  • processing / with f x creates x 0 .
  • the difference between x 0 and x 0 follows a new Laplace distribution with zero mean and variance of approximately—
  • Property 4 By processing a natural scene image with a high pass filter f 2 , any pixel value from the processed image will also follow a Laplace distribution. 0 - 1 0
  • f 2 is another very important tool which can be used to find high frequency content of images.
  • the pixel x i is labeled x after filtering the image / with f 2 .
  • the value of x 0 will follow another Laplace distribution with zero mean and variance— .
  • V As can be observed in FIG. 5a, although different image content produces various specific relationships between V and blurriness, we notice a regularity consisting of an increasing blurriness accompanied by a decreasing V . Having identified this regularity, we use V as a feature to model the blurriness of images containing the same content, since it does not necessarily differentiate blurriness well for images with different content. To provide a more robust feature for handling different image content, we adopt an alternative feature: V—V i . Given any image / , we blur using f x to obtain the blurred image I x . We form V 1 by subsequently filtering I x with f 2 and calculating the variance of the resulting pixel values.
  • V—V 1 is a better blurriness feature than V since it reduces the scale along the feature axis, however it is still not robust to image content. Normalizing V—V 1 by V , we obtain our desired blurriness feature, ⁇ ⁇ , as defined in (7).
  • FIG. 5c A visualization of this feature is shown in FIG. 5c.
  • the curves representing different image content have a more regular relationship between blurriness and our blurriness feature. This denser coupling signifies that this feature can help alleviate the image content issue discussed earlier.
  • Section 4 we use the LIVE image database to show that our feature provides a better characterization of blurriness. Independent of the content of an image, we observe a decrease in our blurriness feature, ⁇ ⁇ , as the blurriness of an image increases.
  • V the variance of the coefficients, V , by processing a noisy image / with filter f 2 . Further processing / with filter f x we get / j , a denoised version of / .
  • V 1 is calculated as the variance of the pixel values after processing I x with f 2 . Since I x has less noise than image / , V will be larger than V x .
  • V—V 1 as a noise feature, which maintains a good response to noise for images with the same content. Experimentation verifies that the larger the value of V—V x , the noisier the image. For images containing different content, the value of V— V 1 will not always be the same even though images may have the same noisiness or level of human perception. To address this problem, we apply normalization to obtain (8).
  • Blockiness appears at a block boundary as a byproduct of encoding, decoding, or transmission. If there appears to be block-like artifacts in a frame, Property 1 states that the statistical relationship between two adjacent pixels in the same block will be different than that of two adjacent pixels from different blocks. To make use of this statistical property, the image is partitioned into b s xb s blocks and sampled in the horizontal and vertical directions as shown in (9) and (10). Methods for constructing these two types of down- sampling are shown below.
  • the dark symbols inside a grid correspond to pixels in the resulting sampled sub- images.
  • the different symbols correspond to different sub-images.
  • Another two data sets D 1 and D 2 can be obtained by taking the difference of sub-images s 7 and s 6 and the difference of sub-images s 0 and s 7 , respectively. If blockiness is not present in the image, the pixel values of the data sets D 1 and D 2 should follow a similar Laplace distribution. If we set the same threshold in two Laplace distributions, ⁇ ⁇ ) represents the numbers of pixels which are larger than the threshold in D i . It is apparent that the values of ⁇ ⁇ ) and 3 ⁇ 4 v) should be close for a non-blocky image.
  • ⁇ is introduced as a tuning parameter to allow f Mockiness to be tailored for a variety of situations and is chosen to be 1 in our experimentation.
  • the value of f blockiness should be close to one.
  • Introducing blockiness into a frame will increase the value of f Mockiness ⁇
  • the five curves represent five different images.
  • we randomly add different percentages of blockiness, which we call ⁇ 3 while measuring the value of f hlockiness .
  • f hlockiness increases with respect to increases in ⁇ .
  • the relationship between f Mockiness and ⁇ is modeled as a quadratic function, as shown in (12).
  • a good distortion metric should offer a delineation between clear and distorted images.
  • For the distorted images we employ Gaussian blur, white noise, and JPEG images from the LIVE image database which correspond to blurriness C ), noisiness ⁇ J 2 ) an d blockiness ( ⁇ 3 ) metrics, respectively.
  • the metric values are calculated for both clear and distorted images and displayed in FIG. 8.
  • the dashed line is the density of ⁇ ⁇ for 85 clear images
  • the solid line is the density of ⁇ ⁇ for the same number of Gaussian blur images.
  • These two densities are estimated as a Gaussian kernel by using the two ⁇ ⁇ histograms of clear and Gaussian blur images, respectively.
  • the overlap of the two densities is relatively small, so our blurriness metric is a good candidate to classify between clear and blurry images.
  • Table 4 Comparison of Srocc for three metrics [0108] In Table 4, we calculate the metric values for all blurry, noisy and blocky images from the LIVE image database and obtain their Spearman Rank Order correlation (Srocc). Mylene[4] tried to measure the same distortions: noisiness, blockiness, and blurriness, so we compare the Srocc for metrics from our model with Mylene's model. Our comparison reveals that our metrics are more correlated with human perception.
  • Codebook Construction One element of the codebook is a vector including four values: ⁇ ⁇ , ⁇ 2 , ⁇ 3 and perceptual values denoted as ( , j , C i 2 , C i 3 , C, 4 ).
  • the codebook model there are four important parameters: p , q , r , and k .
  • the first three parameters represent the distance weights for the three artifact types, and the fourth is the number of nearest neighbors used for prediction.
  • FIG. 13 shows, schematically, an illustrative computer 5000 on which any aspect of the present disclosure may be implemented.
  • the computer 5000 includes a processing unit 5001 having one or more processors and a non-transitory computer-readable storage medium 5002 that may include, for example, volatile and/or non-volatile memory.
  • the memory 5002 may store one or more instructions to program the processing unit 5001 to perform any of the functions described herein.
  • the computer 5000 may also include other types of non-transitory computer-readable medium, such as storage 5005 (e.g., one or more disk drives) in addition to the system memory 5002.
  • storage 5005 may also store one or more application programs and/or external components used by application programs (e.g., software libraries), which may be loaded into the memory 5002.
  • the computer 5000 may have one or more input devices and/or output devices, such as devices 5006 and 5007 illustrated in FIG. 13. These devices can be used, among other things, to present a user interface. Examples of output devices that can be used to provide a user interface include printers or display screens for visual presentation of output and speakers or other sound generating devices for audible presentation of output. Examples of input devices that can be used for a user interface include keyboards and pointing devices, such as mice, touch pads, and digitizing tablets. As another example, the input devices 5007 may include a microphone for capturing audio signals, and the output devices 5006 may include a display screen for visually rendering, and/or a speaker for audibly rendering, recognized text.
  • input devices 5006 and 5007 illustrated in FIG. 13 These devices can be used, among other things, to present a user interface. Examples of output devices that can be used to provide a user interface include printers or display screens for visual presentation of output and speakers or other sound generating devices for audible presentation of output. Examples of input devices that can be used for
  • the computer 5000 may also comprise one or more network interfaces (e.g., the network interface 5010) to enable communication via various networks (e.g., the network 5020).
  • networks include a local area network or a wide area network, such as an enterprise network or the Internet.
  • Such networks may be based on any suitable technology and may operate according to any suitable protocol and may include wireless networks, wired networks or fiber optic networks.
  • the quality score is computed for an image to be displayed.
  • a collection of similar images may be displayed on the same device, such as may occur, for example, when a stream of images is displayed as a video.
  • the quality score may be computed for one or more images in the collection.
  • Those quality scores may be used to select parameters of image processing or display, which may be applied to the collection of images.
  • processors may be implemented as integrated circuits, with one or more processors in an integrated circuit component, including commercially available integrated circuit components known in the art by names such as CPU chips, GPU chips, microprocessor, microcontroller, or co-processor.
  • a processor may be implemented in custom circuitry, such as an ASIC, or semicustom circuitry resulting from configuring a programmable logic device.
  • a processor may be a portion of a larger circuit or semiconductor device, whether commercially available, semi-custom or custom.
  • some commercially available microprocessors have multiple cores such that one or a subset of those cores may constitute a processor.
  • a processor may be implemented using circuitry in any suitable format.
  • a computer may be embodied in any of a number of forms, such as a rack-mounted computer, a desktop computer, a laptop computer, or a tablet computer. Additionally, a computer may be embedded in a device not generally regarded as a computer but with suitable processing capabilities, including a Personal Digital Assistant (PDA), a smart phone or any other suitable portable or fixed electronic device.
  • PDA Personal Digital Assistant
  • a computer may have one or more input and output devices. These devices can be used, among other things, to present a user interface. Examples of output devices that can be used to provide a user interface include printers or display screens for visual presentation of output and speakers or other sound generating devices for audible presentation of output. Examples of input devices that can be used for a user interface include keyboards, and pointing devices, such as mice, touch pads, and digitizing tablets. As another example, a computer may receive input information through speech recognition or in other audible format. In the embodiment illustrated, the input/output devices are illustrated as physically separate from the computing device. In some embodiments, however, the input and/or output devices may be physically integrated into the same unit as the processor or other elements of the computing device. For example, a keyboard might be implemented as a soft keyboard on a touch screen. Alternatively, the input/output devices may be entirely disconnected from the computing device, and functionally integrated through a wireless connection.
  • Such computers may be interconnected by one or more networks in any suitable form, including as a local area network or a wide area network, such as an enterprise network or the Internet.
  • networks may be based on any suitable technology and may operate according to any suitable protocol and may include wireless networks, wired networks or fiber optic networks.
  • the various methods or processes outlined herein may be coded as software that is executable on one or more processors that employ any one of a variety of operating systems or platforms. Additionally, such software may be written using any of a number of suitable programming languages and/or programming or scripting tools, and also may be compiled as executable machine language code or intermediate code that is executed on a framework or virtual machine.
  • the invention may be embodied as a computer readable storage medium (or multiple computer readable media) (e.g., a computer memory, one or more floppy discs, compact discs (CD), optical discs, digital video disks (DVD), magnetic tapes, flash memories, circuit configurations in Field Programmable Gate Arrays or other semiconductor devices, or other tangible computer storage medium) encoded with one or more programs that, when executed on one or more computers or other processors, perform methods that implement the various embodiments of the invention discussed above.
  • a computer readable storage medium may retain information for a sufficient time to provide computer-executable instructions in a non-transitory form.
  • Such a computer readable storage medium or media can be transportable, such that the program or programs stored thereon can be loaded onto one or more different computers or other processors to implement various aspects of the present invention as discussed above.
  • the term "computer-readable storage medium” encompasses only a computer-readable medium that can be considered to be a manufacture (i.e., article of manufacture) or a machine.
  • the invention may be embodied as a computer readable medium other than a computer-readable storage medium, such as a propagating signal.
  • code means any type of computer code or set of computer-executable instructions that can be employed to program a computer or other processor to implement various aspects of the present invention as discussed above. Additionally, it should be appreciated that according to one aspect of this embodiment, one or more computer programs that when executed perform methods of the present invention need not reside on a single computer or processor, but may be distributed in a modular fashion amongst a number of different computers or processors to implement various aspects of the present invention.
  • Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices.
  • program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types.
  • functionality of the program modules may be combined or distributed as desired in various embodiments.
  • data structures may be stored in computer-readable media in any suitable form.
  • data structures may be shown to have fields that are related through location in the data structure. Such relationships may likewise be achieved by assigning storage for the fields with locations in a computer-readable medium that conveys relationship between the fields.
  • any suitable mechanism may be used to establish a relationship between information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish relationship between data elements.
  • the invention may be embodied as a method, of which an example has been provided.
  • the acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments.
  • a reference to "A and/or B", when used in conjunction with open-ended language such as “comprising” can refer, in one embodiment, to A only (optionally including elements other than B); in another embodiment, to B only (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc.
  • the phrase "at least one,” in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements.
  • At least one of A and B can refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc.

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  • Engineering & Computer Science (AREA)
  • Quality & Reliability (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Compression Or Coding Systems Of Tv Signals (AREA)
  • Image Analysis (AREA)

Abstract

La présente invention porte, selon certains aspects, sur des procédés d'évaluation automatisée de qualité d'image sans utiliser une image de référence. Selon certains modes de réalisation, des mesures quantitatives pour des artéfacts particuliers, qui ont un impact sur la qualité de l'image, tels que des métriques de flou, de caractère bruyant et d'effet de bloc (BNB), sont utilisées pour former un vecteur pour représenter la qualité d'une image électronique donnée. Sur la base de ce vecteur, des entrées dans une structure de données sont sélectionnées. Selon certains modes de réalisation, un algorithme des k voisins les plus proches (k-NN) est utilisé pour mettre en correspondance le vecteur de métriques BNB de l'image électronique avec un score de perception humaine sur la base de différences de vecteur entre les mesures quantitatives pour l'image électronique et des mesures quantitatives similaires pour des images connues auxquelles ont été attribués des scores de perception humaine. Les scores de perception humaine pour les entrées sélectionnées dans l'ensemble de données peuvent ensuite être combinés pour donner un score de qualité pour l'image électronique, ce qui permet d'émuler un score de qualité qui serait attribué par des évaluateurs d'image humaine.
PCT/US2017/053393 2016-09-26 2017-09-26 Procédé d'évaluation de qualité d'image sans référence WO2018058090A1 (fr)

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WO2020014862A1 (fr) * 2018-07-17 2020-01-23 深圳大学 Système et procédé d'évaluation de qualité d'image en l'absence de référence
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CN111179245A (zh) * 2019-12-27 2020-05-19 成都中科创达软件有限公司 图像质量检测方法、装置、电子设备和存储介质
CN111489333A (zh) * 2020-03-31 2020-08-04 天津大学 一种无参考夜间自然图像质量评价方法
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CN111507426A (zh) * 2020-04-30 2020-08-07 中国电子科技集团公司第三十八研究所 基于视觉融合特征的无参考图像质量分级评价方法及装置
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CN111652854A (zh) * 2020-05-13 2020-09-11 中山大学 一种基于图像高频信息的无参考图像质量评价方法
CN111862000A (zh) * 2020-06-24 2020-10-30 天津大学 基于局部平均特征值的图像质量评价方法
CN113450319A (zh) * 2021-06-15 2021-09-28 宁波大学 一种基于klt技术的超分辨率重建图像质量评价方法
CN113519165A (zh) * 2019-03-01 2021-10-19 皇家飞利浦有限公司 生成图像信号的装置和方法
CN113784113A (zh) * 2021-08-27 2021-12-10 中国传媒大学 一种基于短时时空融合网络和长时序列融合网络的无参考视频质量评价方法
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WO2020014862A1 (fr) * 2018-07-17 2020-01-23 深圳大学 Système et procédé d'évaluation de qualité d'image en l'absence de référence
US20200118029A1 (en) * 2018-10-14 2020-04-16 Troy DeBraal General Content Perception and Selection System.
US10730293B1 (en) 2019-02-27 2020-08-04 Ricoh Company, Ltd. Medium classification mechanism
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CN110070539A (zh) * 2019-04-28 2019-07-30 重庆大学 基于信息熵的图像质量评价方法
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CN111489333B (zh) * 2020-03-31 2022-06-03 天津大学 一种无参考夜间自然图像质量评价方法
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CN111507426A (zh) * 2020-04-30 2020-08-07 中国电子科技集团公司第三十八研究所 基于视觉融合特征的无参考图像质量分级评价方法及装置
CN111507426B (zh) * 2020-04-30 2023-06-02 中国电子科技集团公司第三十八研究所 基于视觉融合特征的无参考图像质量分级评价方法及装置
CN111652854A (zh) * 2020-05-13 2020-09-11 中山大学 一种基于图像高频信息的无参考图像质量评价方法
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