Vision Models for Wide Color Gamut Imaging in Cinema

Color gamut extension algorithm for various images based on laser display

In pursuit of enhancing the display performance of gamut extension algorithms across diverse image types while minimizing image dependency, this study introduces a dynamic gamut extension algorithm. The algorithm is designed to extend the sRGB source gamut towards the wide gamut of a laser display. To evaluate its effectiveness, psychophysical experiments were conducted using four distinct image categories: complexions, scenery, objects, and color blocks and bars. The performance of the proposed algorithm was benchmarked against four established color gamut mapping algorithms. The comparative analysis revealed that our algorithm excels in handling wide color gamuts, outperforming the alternatives in terms of preference and the preservation of detail richness.

Perceived Color Gamut in Images: From Boundary to Difference

A larger display color gamut volume (CGV) is expected to produce higher perceived brightness and colorfulness of the images displayed. However, display control algorithms such as gamut mapping and color conversion need to be carefully controlled to fully take advantage of the higher luminance and more saturated display primaries. Using RGBW displays (RGB plus a white channel) as a special case in contrast to RGB displays, it is demonstrated that a larger RGB display gamut enclosed by the boundary did not guarantee a larger color gamut perceived in images. Five gamuts with different white channel contributions were simulated, and seven different image contents were curated and rendered on each display. Using a paired comparison experiment with 33 observers, the perceived scales of color gamut as perceived brightness and colorfulness were derived. The results show more correlation with the image-wise than display-wise CGV and can be explained with image color differences. Our findings highlight the importance of considering image contents when optimizing display gamut volume, which can be guided by such image-wise analysis.

Matching visual induction effects on screens of different size

In the film industry, the same movie is expected to be watched on displays of vastly different sizes, from cinema screens to mobile phones. But visual induction, the perceptual phenomenon by which the appearance of a scene region is affected by its surroundings, will be different for the same image shown on two displays of different dimensions. This phenomenon presents a practical challenge for the preservation of the artistic intentions of filmmakers, because it can lead to shifts in image appearance between viewing destinations. In this work, we show that a neural field model based on the efficient representation principle is able to predict induction effects and how, by regularizing its associated energy functional, the model is still able to represent induction but is now invertible. From this finding, we propose a method to preprocess an image in a screen-size dependent way so that its perception, in terms of visual induction, may remain constant across displays of different size. The potential of the method is demonstrated through psychophysical experiments on synthetic images and qualitative examples on natural images.