What the number is

Go back to part 1's Hough transform: every band is a peak, and the sharper the bands, the taller the peaks. Image quality (IQ, in TSL language) or band contrast (Oxford) is simply the average height of the detected peaks. A perfect lattice diffracts crisply; a lattice full of dislocations diffracts from many slightly-rotated sub-volumes at once, and the bands smear. Damage the crystal below and watch the number fall, long before the indexing fails.

Degrade a lattice, watch IQ answer Same pattern engine as part 1

Lattice damage

IQ (normalised)

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1.00 = pristine lattice

Bands still found

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of 8 requested

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IQ vs damage Current damage Indexing starts failing

The useful asymmetry: orientation is a threshold measurement, right until the bands are gone, while IQ is a continuous one, sliding downhill from the first dislocation. That is why IQ sees deformation the orientation map shrugs at.

IQ = ⟨ H(θ_i, ρ_i) ⟩ over the N detected bands H is the Hough (Radon) transform height at each detected peak. Oxford's band contrast and band slope are close cousins from the same peaks. The absolute value depends on camera, gain, exposure, binning and background correction, which is why IQ is only ever meaningful within one map, relatively. Keep that; module 3 collects on it.

The map you get for free

Here is a partially recrystallised alloy: soft, dislocation-free new grains growing into a deformed matrix. The orientation map paints all grains with the same confidence. The IQ map (recorded in the same scan, at zero extra cost) sorts them instantly: bright recrystallised islands, dark strained matrix, slip bands streaking the worst grains, boundaries etched in dark lines.

Orientation vs quality, same scan Click any grain to interrogate it

IQ partition threshold

Recrystallised fraction

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by this threshold · true: –

Selected grain

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– –

Drag the threshold and watch the recrystallised fraction swing. The classification is real (IQ genuinely separates the populations) but the number depends on where you cut, exactly like part 3's grain-size threshold. Papers that report "X% recrystallised by EBSD" without the criterion are reporting a slider position.

Go deeper: what IQ maps have found

The classic uses: recrystallised-fraction measurement (Tarasiuk's IQ-distribution method and its descendants), slip-band and deformation-structure imaging in single maps, revealing fine twins and sub-resolution boundaries as dark lines the orientation map missed, phase discrimination when two phases index alike but diffract with different perfection, and FIB or polishing damage assessment. The pattern is always the same: IQ surfaces gradients of lattice perfection, whatever their cause: a channel orthogonal to orientation.

For quantitative strain there are better tools sitting one step up the sophistication ladder: kernel average misorientation (KAM) turns the orientation data itself into a plastic-strain proxy via geometrically necessary dislocations, and HR-EBSD cross-correlation measures elastic strain tensors at the 10⁻⁴ level. IQ is the quick look; those are the measurements.

What it is not

IQ is a single scalar fed by everything at once. Before reading it as "strain", subtract the other authors: surface preparation, which multiplies the whole map; topography and contamination, which write their own features; and the crystal itself: even a perfect, undeformed polycrystal shows grain-to-grain IQ contrast, because band sharpness depends on orientation.

Three maps that look like strain Only one of them is

Map mean IQ

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Grain-to-grain spread

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std of grain means

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The undeformed control is the one that surprises people: real grain-to-grain IQ contrast, zero damage anywhere. If your "strained grains" correlate suspiciously with orientation, or vanish after a repolish, the IQ map was talking about something else.

The thing to remember

Use IQ within one map, relatively, as a detector of where to look; then confirm with a channel that has units: misorientation statistics, KAM, HR-EBSD, or plain TEM. Pattern quality is the best free data in the scan and the easiest to over-read. Both facts at once.

A closing word for the series

Seven pages, one theme: every EBSD product (the Euler angles, the colours, the boundaries, the textures, the cleaned maps, the resolution, and this grayscale) is a measurement plus a convention plus a choice. The measurements are superb. The conventions are fine. The choices just belong in the caption.

Start of the series · Part 1

From Kikuchi bands to an orientation

Where every pixel began: the pattern, the Hough transform, and the vote, run unhurried, in front of you.

Part 5

Cleaning data honestly

The other place where a choice quietly becomes a number, with the truth on file to show exactly when.

Sources & further reading

S. I. Wright, M. M. Nowell, “EBSD image quality mapping,” Microsc. Microanal. 12, 72 (2006): the definitive treatment: what IQ responds to, including the orientation dependence in module 3.
J. Tarasiuk, Ph. Gerber, B. Bacroix, “Estimation of recrystallized volume fraction from EBSD data,” Acta Mater. 50, 1467 (2002): recrystallised-fraction partitioning from the IQ distribution, done carefully.
S. I. Wright, M. M. Nowell, D. P. Field, “A review of strain analysis using electron backscatter diffraction,” Microsc. Microanal. 17, 316 (2011): IQ, KAM and friends, ranked by what they actually measure.
M. Calcagnotto, D. Ponge, E. Demir, D. Raabe, “Orientation gradients and geometrically necessary dislocations in ultrafine grained dual-phase steels studied by 2D and 3D EBSD,” Mater. Sci. Eng. A 527, 2738 (2010): KAM as the quantitative step past IQ.
A. J. Wilkinson, G. Meaden, D. J. Dingley, “High-resolution elastic strain measurement from electron backscatter diffraction patterns,” Ultramicroscopy 106, 307 (2006): HR-EBSD: strain with real units, from the same patterns.
X. Tao, A. Eades, “Errors, artifacts, and improvements in EBSD processing and mapping,” Microsc. Microanal. 11, 79 (2005): the artefact catalogue every quality-map reader should know.