Classifications

• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N17/00—Diagnosis, testing or measuring for television systems or their details
  • H04N17/004—Diagnosis, testing or measuring for television systems or their details for digital television systems
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; 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 OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V20/00—Scenes; Scene-specific elements
  • G06V20/40—Scenes; Scene-specific elements in video content
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
  • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
  • H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
  • H04N19/103—Selection of coding mode or of prediction mode
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
  • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
  • H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
  • H04N19/103—Selection of coding mode or of prediction mode
  • H04N19/114—Adapting the group of pictures [GOP] structure, e.g. number of B-frames between two anchor frames
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
  • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
  • H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
  • H04N19/154—Measured or subjectively estimated visual quality after decoding, e.g. measurement of distortion
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
  • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
  • H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
  • H04N19/177—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a group of pictures [GOP]
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
  • H04N19/48—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using compressed domain processing techniques other than decoding, e.g. modification of transform coefficients, variable length coding [VLC] data or run-length data
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
  • H04N19/85—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using pre-processing or post-processing specially adapted for video compression
  • H04N19/89—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using pre-processing or post-processing specially adapted for video compression involving methods or arrangements for detection of transmission errors at the decoder
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
  • G06F2218/12—Classification; Matching
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/10—Image acquisition modality
  • G06T2207/10016—Video; Image sequence

Definitions

• This invention relates to video quality measurement, and more particularly, to a method and apparatus for determining an objective video quality metric.
• the present principles provide a method for estimating visual quality of a video sequence, comprising: accessing spatial distortion for frames in a plurality of sliding windows that include a current frame in the video sequence; determining a factor indicating at least one of a large distortion density and a representative artifact level for each sliding window responsive to respective spatial distortion for frames in the each sliding window; determining temporal distortion for the current frame responsive to the determined factor; and determining the visual quality of the video sequence responsive to the temporal distortion of the current frame as described below.
• the present principles also provide an apparatus for performing these steps.
• the present principles provide a method for estimating visual quality of a video sequence, comprising: accessing spatial distortion for frames in a plurality of sliding windows that include a current frame in the video sequence; determining a factor indicating at least one of a large distortion density and a representative artifact level for each sliding window responsive to respective spatial distortion for frames in the each sliding window; determining a maximum of the factors for the plurality of sliding windows; determining temporal distortion for the current frame responsive to the maximum factor; and determining the visual quality of the video sequence responsive to the temporal distortion of the current frame as described below.
• the present principles also provide an apparatus for performing these steps.
• the present principles also provide a computer readable storage medium having stored thereon instructions for estimating visual quality of a video sequence according to the methods described above.
• FIG. 1A is a pictorial example depicting spatial artifact levels for individual frames in an exemplary video sequence
• FIG. 1 B is a pictorial example depicting perceived temporal quality for individual frames in the exemplary video sequence.
• FIGs. 2A, 2B, and 2C are pictorial examples depicting sliding windows used in video quality modeling, in accordance with an embodiment of the present principles.
• FIG. 3A is a pictorial example depicting spatial artifact levels for individual frames in another exemplary video sequence
• FIGs. 3B and 3C are pictorial examples depicting dominant distortion in a frame's neighborhood and estimated temporal distortion, respectively, in accordance with an embodiment of the present principles.
• FIG. 4A is a pictorial example depicting spatial artifact levels for individual frames in another exemplary video sequence
• FIGs. 4B and 4C are pictorial examples depicting the highest large distortion density in a frame's neighborhood and estimated temporal distortion, respectively, in accordance with an embodiment of the present principles.
• FIG. 5A is a pictorial example depicting spatial artifact levels for individual frames in another exemplary video sequence
• FIGs. 5B and 5C are pictorial examples depicting the highest large distortion density in a frame's neighborhood and estimated temporal distortion, respectively, in accordance with an embodiment of the present principles.
• FIG. 6 is a flow diagram depicting an exemplary method for modeling temporal distortion at frame n, in accordance with an embodiment of the present principles.
• FIG. 7 is a block diagram depicting an exemplary video quality monitor, in accordance with an embodiment of the present principles.
• FIG. 8 is a block diagram depicting an exemplary video processing system that may be used with one or more implementations.
• spatial artifact to denote artifact perceived in a picture in a video sequence when the picture is viewed independently of other pictures in the video sequence
• temporal artifact to denote artifact that is perceived in a picture of a video sequence when pictures in the video sequence are continuously displayed
• spatial distortion or “spatial quality” to denote distortion or quality perceived in a picture when the picture is viewed independently of other pictures in a video sequence
• temporal distortion or “temporal quality” to denote distortion or quality that is perceived in a picture of a video sequence when pictures in the video sequence are continuously displayed.
• a picture When assessing spatial distortion, a picture is viewed independently of other pictures in the video sequence, for a period of time that is long enough for a viewer to recognize image content and distortion. This is different from assessing temporal distortion, wherein pictures are continuously displayed.
• ds(n) can be obtained by various image quality assessment methods, for example, but not limited to, a full-reference or no-reference method, and a method in a pixel domain or at a bitstream level.
• spatial distortion can be estimated as the overall visible artifact level, caused by initial and/or propagated visible artifact, as disclosed in a commonly owned PCT application, entitled “Video quality assessment at a bitstream level” by N. Liao, Z. Chen, and K. Xie (PCT/CN2012/078766, Attorney Docket No.
• Spatial artifact in pictures needs to last for a period of time so that eyes can fix on and recognize it as artifact.
• the pictures are part of a video sequence and each is displayed only for a very short period of time (for example, a period of 1/frame_rate when the video is played in real time)
• the perceived video distortion at the time instant of frame n i.e., temporal distortion at frame n, dt(n)
• FIG. 1A shows spatial artifact levels of individual frames in the video sequence
• FIG. 1 B shows temporal quality of individual frames in the video sequence. More specifically, FIG. 1A shows spatial artifact levels of the frames when the exemplary video sequence suffers from packet losses.
• the spatial artifact may be sporadic in the video sequence, for example, the spatial artifact seen at frames 74, 77, 215, 261 , and 262.
• the spatial artifact may also occur in a burst, such as the artifact seen around frames 106-1 1 1 .
• FIG. 1 B shows temporal quality when the frames of the video sequence are displayed continuously, wherein score 100 corresponds to the best quality.
• the quality score may be at a different scale.
• the accurate curve of temporal quality may be obtained using a subjective test method, for example, but not limited to, Single Stimulus Continuous Quality Evaluation (SSCQE), as defined in ITU-R BT 500.
• SSCQE Single Stimulus Continuous Quality Evaluation
• FIG. 1 A frames 74 and 77 have strong spatial artifact when these two frames are viewed independently.
• the artifact at these two frames becomes invisible when the video is displayed continuously, and thus, frames 74 and 77 are at the best quality level when viewed continuously as shown in FIG. 1 B.
• strong spatial artifact may not always correspond to high temporal distortion.
• one frame of a video sequence may appear to have good quality when viewed independently of other frames, but may present very strong temporal distortion (for example, motion jitter) when the video sequence is displayed continuously. That is, small spatial distortion may not always correspond to small perceived temporal distortion (i.e., higher temporal quality).
• very strong temporal distortion for example, motion jitter
• the present principles provide a method and apparatus for accurately modeling temporal quality from spatial distortion for individual frames.
• the present principles consider the context that affects how a viewer identifies temporal distortion, wherein the context includes, for example, but not limited to, the duration and the pattern of the distortion, and texture and object's motion that are recognized by a viewer via watching the neighboring frames.
• the perceived temporal distortion can be modeled using a sliding window approach. As shown in FIGs.
• a sliding window of L 0 frames that includes frame n (denoted asS i n ) starts at frame (n - i)and ends at frame (n - i + L 0 - 1), 0 ⁇ i ⁇ L 0 .
• Artifact existing outside the sliding windows is regarded as having little contribution to the visibility of the temporal artifact of a current frame.
• the perceived temporal distortion of a current frame is mainly affected by frames with large distortion (i.e., distortion level exceeds a certain threshold) that are close by.
• MD i n median ⁇ ds(j), frame j E S i n ). (1 ) A median function examines neighboring frames to decide whether or not the spatial distortion of the current frame is representative of its surroundings and rejects extreme distortion levels (outliers). That is, we may consider MD ⁇ as a
• a maximum function can be used to identify the dominant distortion as the maximum of median distortion values among the sliding windows ⁇ S i n , 0 ⁇ i ⁇ L 0 ):
• FIG. 3A shows spatial artifact levels for frames in an exemplary video sequence
• FIGs. 3B and 3C show results after applying Eqs. (2) and (3), respectively.
• Eqs. (2) and (3) show results after applying Eqs. (2) and (3), respectively.
• the maximum of median distortion values avoid extreme values as shown in FIG. 3B. Consequently, as can be seen from FIG. 3C, spatial artifact levels that are much larger than neighboring ones, for example, at frames 86, 125, and 166, are not present in the estimated temporal distortion.
• the estimated temporal distortion levels have smaller variations from frame to frame than the spatial distortion levels.
• dist(n) w n x ds(n)/f(dist(n)), (7) wherein dist(n) is the distance between frame n and the closest frame with large distortion in a sliding window corresponding to the highest large distortion density. If there is no other frame with large distortion in the corresponding sliding window, we set dist(n) to a very big value, for example, 1000. That is, when there is only one frame in the sliding window with large distortion, we consider the distortion as less visible and set dt(n) to a very small value.
• FIG. 4A shows spatial artifact levels for frames in an exemplary video sequence
• U (number of macro blocks per frame)/100.
• the values of L 0 and U may vary with configurations, for example, with the GOP length, video resolution, and the frame rate.
• FIG. 5A shows spatial artifact levels for frames in another exemplary video sequence
• FIGs. 5B and 5C show results after applying Eqs. (5) and (7), respectively.
• the median distortion value and the large distortion density approaches can be combined to estimate the temporal distortion for frame n as:
• temporal distortion based on the human vision property that eyes need a period of time that is long enough to recognize artifact.
• the temporal distortion may also be affected by other factors, for example, but not limited to, motion jerkiness. Consequently, the temporal distortion estimated as above may need to be adjusted to consider other factors.
• dt'(n) dt(n) + c x dt 2 (n), where dt 2 (n)is the distortion caused by motion jerkness.
• FIG. 6 illustrates an exemplary method 600 for modeling temporal distortion at frame n, according to the present principles.
• Method 600 starts at step 605.
• For sliding window Si ,n it calculates the median distortion value at step 610, for example, using Eq. (1 ), and calculates the large distortion density at step 620, for example, using Eq. (4). It checks whether more sliding window needs to be processed at step 630. If yes, it returns the control to step 610. Otherwise, at step 640, it calculates the maximum of median distortion values in all sliding windows for frame n, for example, using Eq. (2). At step 650, it calculates the highest large distortion density in all sliding windows for frame n, for example, using Eq. (5).
• step 660 it estimates the temporal distortion for frame n, for example, using Eq. (3), (6), or (8).
• the distance between frame n and the closest frame with large distortion may be considered at step 660, for example, using Eq. (7).
• Method 600 ends at step 699.
• both the maximum of median distortion values and the highest large distortion density are used to estimate temporal distortion.
• only the maximum of median distortion values is used to estimate the temporal distortion. That is, steps 620 and 650 are not needed, and step 660 estimates the temporal distortion based on the maximum of median distortion values, for example, using Eq. (3).
• only the highest large distortion density is used to estimate the temporal distortion. That is, steps 610 and 640 are not needed, and step 660 estimates the temporal distortion based on the highest large distortion density, for example, using Eq. (6) or Eq. (7).
• Method 600 or its variations may proceed in a different order of steps, for example, step 620 may be performed before step 610, step 650 may be performed before step 640.
• the video quality modeling methods according to the present principles can be applied to measure video quality when a video sequence suffers from
• FIG. 7 depicts a block diagram of an exemplary video quality monitor 700.
• the input of apparatus 700 may include a transport stream that contains the bitstream.
• the input may be in other formats that contains the bitstream.
• Demultiplexer 710 obtains packet layer information from the bitstream.
• Decoder 720 parses the input stream to obtain more information. Decoder 720 may or may not reconstruct the pictures. In other embodiments, the decoder may perform the functions of the demultiplexer. Using the decoded information, the spatial artifact levels are estimated in spatial artifact level estimator730. Based on the estimated parameters, temporal distortion levels are estimated at temporal distortion estimator 740, for example, using method 600. A quality predictor 750 then pools temporal distortion levels for individual frames into a quality score for the video sequence. The quality predictor 750 may consider other types of artifacts and the property of human visual property.
• the video quality monitor700 may be used, for example, in ITU-T
• IPTV and mobile video streaming also called HR (High Resolution)scenario and LR (Low Resolution)scenario respectively.
• HR High Resolution
• LR Low Resolution
• the input to the P.N BAMS VQM Video Quality Model
• the output is an objective MOS score (Mean Opinion Score).
• MOS score Mean Opinion Score
• a major target application of P.NBAMS work is to monitor video quality in a set-top box (STB) or gateway.
• P.NBAMS mode 1 model only uses bitstream information, and mode 2 model may decode part or all of the video sequence, and the pixel information is used for visual quality prediction in addition to parsing the bitstream information in order to improve the prediction accuracy.
• a video transmission system or apparatus 800 is shown, to which the features and principles described above may be applied.
• a processor 805 processes the video and the encoder 810 encodes the video.
• the bitstream generated from the encoder is transmitted to a decoder 830 through a distribution network 820.
• a video quality monitor may be used at different stages.
• a video quality monitor 840 may be used by a content creator.
• the estimated video quality may be used by an encoder in deciding encoding parameters, such as mode decision or bit rate allocation.
• the content creator uses the video quality monitor to monitor the quality of encoded video. If the quality metric does not meet a pre-defined quality level, the content creator may choose to re-encode the video to improve the video quality. The content creator may also rank the encoded video based on the quality and charges the content accordingly.
• a video quality monitor850 may be used by a content distributor.
• a video quality monitor may be placed in the distribution network. The video quality monitor calculates the quality metrics and reports them to the content distributor. Based on the feedback from the video quality monitor, a content distributor may improve its service by adjusting bandwidth allocation and access control.
• a video quality monitor 860 may be used by a user device. For example, when a user device searches videos in Internet, a search result may return many videos or many links to videos corresponding to the requested video content. The videos in the search results may have different quality levels. A video quality monitor can calculate quality metrics for these videos and decide to select which video to store. In another example, the user device may have access to several error concealment techniques. A video quality monitor can calculate quality metrics for different error concealment techniques and automatically choose which concealment technique to use based on the calculated quality metrics.
• the implementations described herein may be implemented in, for example, a method or a process, an apparatus, a software program, a data stream, or a signal. Even if only discussed in the context of a single form of implementation (for example, discussed only as a method), the implementation of features discussed may also be implemented in other forms (for example, an apparatus or program).
• An apparatus may be implemented in, for example, appropriate hardware, software, and firmware.
• the methods may be implemented in, for example, an apparatus such as, for example, a processor, which refers to processing devices in general, including, for example, a computer, a microprocessor, an integrated circuit, or a programmable logic device. Processors also include communication devices, such as, for example, computers, cell phones, portable/personal digital assistants ("PDAs”), and other devices that facilitate communication of information between end-users.
• PDAs portable/personal digital assistants
• the appearances of the phrase “in one embodiment” or “in an embodiment” or “in one implementation” or “in an implementation”, as well any other variations, appearing in various places throughout the specification are not necessarily all referring to the same embodiment.
• this application or its claims may refer to "determining" various pieces of information. Determining the information may include one or more of, for example, estimating the information, calculating the information, predicting the information, or retrieving the information from memory. Further, this application or its claims may refer to "accessing" various pieces of information. Accessing the information may include one or more of, for example, receiving the information, retrieving the information (for example, from memory), storing the information, processing the information, transmitting the information, moving the information, copying the information, erasing the information, calculating the information, determining the information, predicting the information, or estimating the information.
• Receiving is, as with “accessing”, intended to be a broad term.
• Receiving the infornnation may include one or more of, for example, accessing the information, or retrieving the information (for example, from memory).
• “receiving” is typically involved, in one way or another, during operations such as, for example, storing the information, processing the information, transmitting the information, moving the information, copying the information, erasing the information, calculating the information, determining the information, predicting the information, or estimating the information.
• implementations may produce a variety of signals formatted to carry information that may be, for example, stored or transmitted.
• the information may include, for example, instructions for performing a method, or data produced by one of the described implementations.
• a signal may be formatted to carry the bitstream of a described embodiment.
• Such a signal may be formatted, for example, as an electromagnetic wave (for example, using a radio frequency portion of spectrum) or as a baseband signal.
• formatting may include, for example, encoding a data stream and modulating a carrier with the encoded data stream.
• the information that the signal carries may be, for example, analog or digital information.
• the signal may be transmitted over a variety of different wired or wireless links, as is known.
• the signal may be stored on a processor-readable medium.

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• 2013
  • 2013-06-09 US US14/763,940 patent/US9716881B2/en not_active Expired - Fee Related
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