Banding is a type of quantisation artefact that appears when a low-texture region of an image is coded with insufficient bitdepth. Banding artefacts are well-studied for standard dynamic range (SDR), but are not well-understood for high dynamic range (HDR). To address this issue, we conducted a psychophysical experiment to characterise how well human observers see banding artefacts across a wide range of luminances (0.1 cd/m2–10,000 cd/m2). The stimuli were gradients modulated along three colour directions: black-white, red-green, and yellow-violet. The visibility threshold for banding artefacts was the highest at 0.1 cd/m2, decreased with increasing luminance up to 100 cd/m2, then remained at the same level up to 10,000 cd/m2. We used the results to develop and validate a model of banding artefact detection. The model relies on the contrast sensitivity function (CSF) of the visual system, and hence, predicts the visibility of banding artefacts in a perceptually accurate way.
The idea of contrast at a pixel, including contrast in colour or higher-dimensional image data, has traditionally been associated with the Structure Tensor, also named the di Zenzo matrix or Harris matrix. This 2 × 2 array encapsulates how colour-channel first-derivatives give rise to change in any spatial direction in x, y. The di Zenzo or Harris matrix Z has been put to use in several different applications. For one, the Spectral Edge method for image fusion uses Z for a putative colour image, along with the Z for higher-dimensional data, to produce an altered RGB image which properly has exactly the same Z as that of high-D data. So e.g. the contrast from RGB + NIR images can be fused such that Z in RGB takes on the same values as Z for 4-D data. As well, Z has been used as the foundation for the Harris interest-point or corner-point detector. However, a competing definition for Z is the 2 × 2 Hessian matrix, formed from second-derivative values rather than first derivatives. In this paper we develop a novel Z which in the first place utilizes the Harris Z, but then goes on to modify Z by adding some information from the Hessian. Moreover, here we consider an extension to a Hessian for colour or higher-D image data which treats colour channels not as simply to be added, but in a colour formulation that generates the Hessian from a colour vector. For image fusion, experiments are carried out on three datasets of 50 images each. Using the modified version of Z that includes Hessian information, results are shown to retain more details and also generate fused images that have smaller CIELAB errors from the original RGB. Using the new Z in corner-detection, the novel colour Hessian produces interest points that are more accurate, and as well generates fewer mistake points.
'Crispening' is an effect whereby subjects perception of luminance is biased away from the background luminance level. The effect is strong, but may be reduced or abolished by the addition of a hue shift or an annulus that separates the tests stimuli from the background [16, 18]. In this paper we investigate whether the 'crispening' effect may arise from a simple gain mechanism that decreases sensitivity at luminance levels away from the background luminance level. The model takes as input the threshold versus intensity function, then decreases sensitivity via a gain mechanism. The supra-threshold percept is then estimated via Fechnerian integration of the resulting thresholds. We find that the model can predict subjects' luminance nonlinearities in all conditions as long as a parameter that controls the degree of gain is allowed to vary. Perhaps more interestingly, we find that the model can explain the luminance nonlinearity in the case where an annulus is present by treating the annulus as an additional background luminance level that also mediates gain. When multiple background luminance levels are included, the gain no longer produces the distinctive 'crispening' effect, although the gain still substantially affects the shape of the luminance nonlinearity. This may account for why 'crispening' is not observed when complex, real world scenes are investigated [2].