Temporal Psychovisual Modulation: a new paradigm of information display

We report on a new paradigm of information display that greatly extends the utility and versatility of current optoelectronic displays. The main innovation is to let a display of high refresh rate optically broadcast so-called atom frames, which are designed through non-negative matrix factorization to form bases for a class of images, and different viewers perceive selfintended images by using display-synchronized viewing devices and their own human visual systems to fuse appropriately weighted atom frames. This work is essentially a scheme of temporal psychovisual modulation in visible spectrum, using an optoelectronic modulator coupled with a biological demodulator

Window of Visibility in the Display and Capture Process

In normal conditions, the Critical Flicker Frequency is usually 60Hz. But in some special conditions, such as low spatial frequency and high contrast between frames, these special conditions have high probability to occur in some TPVMbased applications. So it’s extremely important to verify if a visual signal with a combination of temporal and spatial frequency can be recognize by human eyes. Based on the research in the last paper ’ ’Window of Visibility’ inspired security lighting system’, this paper introduces the measuring method of WoV of humaneyes. In this paper we will measure critical flicker frequency in low spatial frequency and high contrast conditions, and we can witness a different conclusion from the normal conditions

How is Gaze Influenced by Image Transformations? Dataset and Model

Data size is the bottleneck for developing deep saliency models, because collecting eye-movement data is very time consuming and expensive. Most of current studies on human attention and saliency modeling have used high quality stereotype stimuli. In real world, however, captured images undergo various types of transformations. Can we use these transformations to augment existing saliency datasets? Here, we first create a novel saliency dataset including fixations of 10 observers over 1900 images degraded by 19 types of transformations. Second, by analyzing eye movements, we find that observers look at different locations over transformed versus original images. Third, we utilize the new data over transformed images, called data augmentation transformation (DAT), to train deep saliency models. We find that label preserving DATs with negligible impact on human gaze boost saliency prediction, whereas some other DATs that severely impact human gaze degrade the performance. These label preserving valid augmentation transformations provide a solution to enlarge existing saliency datasets. Finally, we introduce a novel saliency model based on generative adversarial network (dubbed GazeGAN). A modified UNet is proposed as the generator of the GazeGAN, which combines classic skip connections with a novel center-surround connection (CSC), in order to leverage multi level features. We also propose a histogram loss based on Alternative Chi Square Distance (ACS HistLoss) to refine the saliency map in terms of luminance distribution. Extensive experiments and comparisons over 3 datasets indicate that GazeGAN achieves the best performance in terms of popular saliency evaluation metrics, and is more robust to various perturbations. Our code and data are available at: https://github.com/CZHQuality/Sal-CFS-GAN