Image and video banding assessment

1. A method for assessing banding effects in an image or video, comprising:

decomposing the image or each frame of the video into luminance or color channels;

computing signal activity at each spatial location in each channel;

determining a significance of the signal activity at each spatial location by comparing each element of the signal activity with a significance threshold;

detecting banding pixels as those pixels of the image or frame having significant signal activity at their respective spatial locations and having non-significant signal activity of at least a minimum threshold percentage of neighboring pixels to the respective spatial locations; and

creating a banding map by:

marking, on the map, the spatial locations of the detected banding pixels;

normalizing the signal activity at the banding pixel locations using the significance threshold to generate normalized signal activity; and

determining an intensity level of each of the banding pixels in the banding map as the normalized signal activity of the respective pixel location.

2. The method of claim 1 , further comprising measuring the signal activity at a local point or region by:

defining a local window surrounding the local point or region; and

computing statistical features for activity or energy estimation within the local window.

3. The method of claim 1 , further comprising determining the significance threshold using human visual system just noticeable difference (JND) values.

4. The method of claim 1 , further comprising adjusting the significance threshold using maximum and minimum luminance values and luminance steps of a display device configured to present the image or video.

5. The method of claim 1 , further comprising adjusting the significance threshold using a bit-depth of the image or video.

6. The method of claim 1 , wherein the image or video is encoded in a standard dynamic range (SDR) or high dynamic range (HDR) format, and further comprising adjusting the significance threshold using maximum, minimum, and mean luminance values of the image or video content according to values contained in metadata included in the SDR or HDR format.

7. The method of claim 1 , further comprising adjusting the significance threshold using one or more of an optical-electro transfer function (OETF) or an electro-optical transfer function (EOTF) used in one or more of acquisition or display of the image or video.

8. The method of claim 1 , further comprising adjusting the significance threshold using one or more of a pixel color value, a color space, a color downsampling format, or a color gamut of the image or video.

9. The method of claim 1 , further comprising adjusting the significance threshold using one or more of a viewing distance, a viewing angle, and an ambient light level of a viewing environment of the image or video.

10. The method of claim 1 , further comprising trimming the detected banding pixels by:

comparing the signal activity with a high activity threshold, the high activity threshold being greater than the significance threshold; and

removing the banding pixels that have higher signal activity than the high activity threshold.

11. The method of claim 1 , further comprising:

computing a banding spread measure by computing a percentage of regions in the image or video frame that contain banding pixels; and

computing a banding strength measure by computing aggregated signal activities at the banding pixels.

12. The method of claim 11 , further comprising computing a per-image or per-frame banding level measure by:

generating per-color channel banding level by combining banding spread and banding strength measures for each color channel; and

generating the per-image or per-frame banding level measure by combining per-color channel band level measures from one or more color channels of the image or video frame.

13. The method of claim 12 , further comprising, in assessing banding effect in the video, aggregating per-frame banding spread, per-frame banding strength and per-frame banding level measures to banding spread, banding strength and banding level measures at larger time scales including per group-of-picture (per-GoP), per-scene, per video asset, per-second, per-minute, per-hour, and any other time scales.

14. The method of claim 1 , further comprising, in assessing the banding effects of the image or video as a test image or video, where a pristine-quality reference image or video is available:

assessing a banding effect of the reference image or video;

assessing a banding effect of the test image or video without using the reference image or video; and

adjusting a banding effect assessment result of the test image or video by discounting a part of the image or video where banding effect is detected in both the reference and test image or video.

15. A system for assessing banding effect in an image or video, comprising:

a computing device programmed to

decompose the image or each frame of the video into luminance or color channels;

compute signal activity at each spatial location in each channel;

determine a significance of the signal activity at each spatial location by comparing each element of the signal activity with a significance threshold; and

detect banding pixels as those pixels of the image or frame having significant signal activity at their respective spatial locations and having non-significant signal activity of at least a minimum threshold percentage of neighboring pixels to the respective spatial locations; and

creating a banding map by performing operations including to:

mark, on a map, the spatial locations of the detected banding pixels,

normalize the signal activity at the banding pixel locations using the significance threshold to generate normalized signal activity, and

determine an intensity level of each of the banding pixels in the banding map as the normalized signal activity of the respective pixel location.

16. The system of claim 15 , wherein the computing device is further programmed to measure the signal activity at a local point or region using operations including to:

define a local window surrounding the local point or region; and

compute statistical features for activity or energy estimation within the local window.

17. The system of claim 15 , wherein the computing device is further programmed to determine the significance threshold using human visual system just noticeable difference (JND) values.

18. The system of claim 15 , wherein the computing device is further programmed to adjust the significance threshold using maximum and minimum luminance values and luminance steps of a display device configured to present the image or video.

19. The system of claim 15 , wherein the computing device is further programmed to adjust the significance threshold using a bit-depth of the image or video.

20. The system of claim 15 , wherein the image or video is encoded in a standard dynamic range (SDR) or high dynamic range (HDR) format, and the computing device is further programmed to adjust the significance threshold using maximum, minimum, and mean luminance values of the image or video content according to values contained in metadata included in the SDR or HDR format.

21. The system of claim 15 , wherein the computing device is further programmed to adjust the significance threshold using one or more of an optical-electro transfer function (OETF) or an electro-optical transfer function (EOTF) used in one or more of acquisition or display of the image or video.

22. The system of claim 15 , wherein the computing device is further programmed to adjust the significance threshold using one or more of a pixel color value, a color space, a color downsampling format, or a color gamut of the image or video.

23. The system of claim 15 , wherein the computing device is further programmed to adjust the significance threshold using one or more of a viewing distance, a viewing angle, and an ambient light level of a viewing environment of the image or video.

24. The system of claim 15 , wherein the computing device is further programmed to trim the detected banding pixels by performing operations including to:

compare the signal activity with a high activity threshold, the high activity threshold being greater than the significance threshold; and

remove the banding pixels that have higher signal activity than the high activity threshold.

25. The system of claim 15 , wherein the computing device is further programmed to:

compute a banding spread measure by computing a percentage of regions in the image or video frame that contain banding pixels; and

compute a banding strength measure by computing aggregated signal activities at the banding pixels.

26. The system of claim 25 , wherein the computing device is further programmed to compute a per-image or per-frame banding level measure by performing operations including to:

generate per-color channel banding level by combining banding spread and banding strength measures for each color channel; and

generate per-image or per-frame banding level measure by combining per-color channel band level measures from one or more color channels of the image or video frame.

27. The system of claim 26 , wherein the computing device is further programmed to, in assessing banding effect in a video, aggregate per-frame banding spread, per-frame banding strength and per-frame banding level measures to banding spread, banding strength and banding level measures at larger time scales including per group-of-picture (per-GoP), per-scene, per video asset, per-second, per-minute, per-hour, and any other time scales.

28. The system of claim 15 , wherein the computing device is further programmed to, in assessing banding effects of the image or video as a test image or video, where a pristine-quality reference image or video is available:

assess a banding effect of the reference image or video;

assess a banding effect of test image or video without using the reference image or video; and

adjust a banding effect assessment result of the test image or video by discounting a part of the image or video where the banding effect is detected in both the reference and test image or video.