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System for natural language assessment of relative color quality

A natural language and color technology, applied in the field of image quality analysis, which can solve the problems of interpretation experts, time-consuming, and difficult output of MICAM.

Inactive Publication Date: 2012-10-17
PROJECT GIANTS LLC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Previous methods for detecting such problematic color changes have required: a) viewing the before and after video directly, which is time consuming and sometimes the before and after video sources are not simultaneously available for viewing); b) using a vectorscope ) or "color", which needs to be interpreted by a trained human, and may even then be misleading due to the lack of human visual models, i.e. the brightness of the color is missed, so black often appears as a bright color on a vector display; c) "color" (based on YUV UV or RGB) Peak Signal-to-Noise Ratio (PSNR) measurements, which can be automated, but also suffer from the lack of human visual models like vectorscope solutions; d) human visual model type video quality analysis products (such as TEKTRONIX PQA300 Quality Analyzer) can determine whether a perceivable color change has occurred, but it lacks many of the important adaptations needed to accurately predict how colors differ (and in some cases even appear different for certain viewing conditions) mechanism; or e) using a more advanced human visual modeling technique, such as that of the United States of America filed on December 10, 2009, entitled METHOD AND APPARATUS FOR IMPLEMENTING MOVING IMAGE COLOR APPEARANCE MODEL FOR VIDEO QUALITY RATINGS PREDICTION and incorporated herein by reference Moving Image Color Appearance Model (MICAM) as described in patent application 12 / 635,456,
However, MICAM output may be difficult for untrained operators to interpret and some differences in the modified video may be difficult for even experts to detect

Method used

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  • System for natural language assessment of relative color quality
  • System for natural language assessment of relative color quality
  • System for natural language assessment of relative color quality

Examples

Experimental program
Comparison scheme
Effect test

example 1

[0062] Angle: = 13

[0063] Quantity: = Red.12

[0064] Reference value: = red

[0065] Relative magnitude:= magnitude / reference magnitude

[0066] Relative magnitude = 0.12.

[0067] For example, if it is determined that the hue difference angle is 13 (which would fall within "red" n from Table 1), then the opposite hue color falls within "Green Blue" from Table 2. Additionally a magnitude of .12 was determined as shown in the above formula. Diagram this process as Figure 4 440 in.

[0068] The normalized magnitude of the color change is then passed from the magnitude determiner 120 to the natural language selector 130 to select the appropriate quantitative superlative word or phrase from the natural language table 131 according to a set of category quantization thresholds for hue and opposite hue. Picture this as Figure 4 Process 450.

[0069] An example natural language table 131 for a particular color shift threshold is illustrated in Table 3.

[0070] Table 3 (...

example 2

[0091] If the following values ​​are input to the saturation measurement facility 114:

[0092] meanAbsTest_a:=.1 meanAbsRef_a:=.11

[0093] meanAbsTest_b:=.08 meanAbsRef_b:=.1

[0094] Then diffOfMeanAbs_a:=meanAbsTest_a - meanAbsRef_a = -0.01

[0095] diffOfMeanAbs_b:=meanAbsTest_b - meanAbsRef_b = -0.02

[0096] First as described as Figure 5 As in process 510 of , the distance between the test and reference image(s) is determined as given above. Quantization bins for the corresponding distances are then determined in process 520, which means determining which index value corresponds to each diffOfMeanAbs_a and diffOfMeanAbs_b. If two distances are quantized to the same bin (same threshold level of Table 3), then image 3 The natural language selector 130 can be as in Figure 5 The processes shown in 530 and 540 generate the following text for describing certain conditions:

[0097] All in all, the video colors appear to be {superlative 1} {more saturated / less s...

example 3

[0101] If the following values ​​are input to the saturation measurement facility 114:

[0102] meanAbsTest_a:=.3 meanAbsRef_a:=.11

[0103] meanAbsTest_b:=.08 meanAbsRef_b:=.1

[0104] Then diffOfMeanAbs_a:=meanAbsTest_a - meanAbsRef_a = 0.19

[0105] diffOfMeanAbs_b:=meanAbsTest_b - meanAbsRef_b = -0.02

[0106] Here, unlike Example 2, when the quantization bins for "a" and "b" are different according to Table 3, image 3 The Natural Language Selector 130 can generate the following text:

[0107] In summary, the video has {superlative 1} {more saturated / less saturated} red and / or green and {superlative 2} {more saturated / less saturated} blue and / or yellow.

[0108] This becomes, using the data from Example 3:

[0109] "Overall, the video has noticeably more saturated reds and / or greens and slightly less saturated blues and / or yellows."

[0110] Overall change in color variety / variation

[0111] Color Diversity / Change Measurement Facility 116 ( figure 2 ) Deter...

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Abstract

Embodiments of the invention include a system for providing a natural language objective assessment of relative color quality between a reference and a source image. The system may include a color converter that receives a difference measurement between the reference image and source image and determines a color attribute change based on the difference measurement. The color attributes may include hue shift, saturation changes, and color variation, for instance. Additionally, a magnitude index facility determines a magnitude of the determined color attribute change. Further, a natural language selector maps the color attribute change and the magnitude of the change to natural language and generates a report of the color attribute change and the magnitude of the color attribute change. The output can then be communicated to a user in either text or audio form, or in both text and audio forms.

Description

technical field [0001] The present disclosure relates to picture quality analysis and more particularly to a system providing for evaluating relative color quality between reference and source images using natural language. Background technique [0002] Video is often changed from an original video stream to a modified video stream. The dynamics of the change are often related to the bandwidth of the target medium over which the modified video stream will be transmitted. But there are various reasons for modifying a video. Other reasons for processing video include, for example, editing for different displays, compression and decompression, reformatting, video insertion and overlay, minimization of transmission errors, and color modification. [0003] The video industry imposes restrictions on color reproduction in modified video. In other words, the industry establishes the standards that the resulting video must pass in order to be acceptable. A problem that often aris...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04N17/02H04N9/73
CPCH04N17/004G06T7/001
Inventor K.M.费尔古森
Owner PROJECT GIANTS LLC