BiPMAP: A Toolbox for Predictions of Perceived Motion Artifacts on Modern Displays

TL;DR

BiPMAP addresses the need for predicting perceived motion artifacts on modern displays by integrating a two-stream Fourier-domain pipeline with a separable spatiotemporal CSF model. It predicts artifacts such as flicker, judder, edge banding, motion blur, color breakup, and stereoscopic depth distortion, and visualizes continuous versus sampled stimuli to guide design optimization. The toolbox supports non-stereoscopic and stereoscopic predictions, RGB modes, eye movements, and compare-mode benchmarking, enabling rapid exploration of display configurations. Its GPU-accelerated, parameter-rich approach provides a practical framework for guiding the design and manufacture of displays that minimize motion artifacts, with planned extensions to more complex stimuli and image-quality metrics.

Abstract

Presenting dynamic scenes without incurring motion artifacts visible to observers requires sustained effort from the display industry. A tool that predicts motion artifacts and simulates artifact elimination through optimizing the display configuration is highly desired to guide the design and manufacture of modern displays. Despite the popular demands, there is no such tool available in the market. In this study, we deliver an interactive toolkit, Binocular Perceived Motion Artifact Predictor (BiPMAP), as an executable file with GPU acceleration. BiPMAP accounts for an extensive collection of user-defined parameters and directly visualizes a variety of motion artifacts by presenting the perceived continuous and sampled moving stimuli side-by-side. For accurate artifact predictions, BiPMAP utilizes a novel model of the human contrast sensitivity function to effectively imitate the frequency modulation of the human visual system. In addition, BiPMAP is capable of deriving various in-plane motion artifacts for 2D displays and depth distortion in 3D stereoscopic displays.

Figures (9)

• Figure 1: Overview of BiPMAP. (A) Front end with user-defined parameters divided into two components: Setup (configuration inputs) and Parameter Selection (simulation variables). (B) Back-end pipeline for artifact predictions containing two streamlines---Non Stereo (top) and Stereo (bottom)---and a figure-generation step. When "Compare Mode" in the Setup component of (A) is enabled, the Comparison Processor integrates information from past runs and outputs a comparison figure.
• Figure 2: Input parameters in the user interface. The three panels on the left show the interfaces for selecting, respectively, stimulus, display, and viewing parameters. Within each panel, only parameters of the specified group can be defined. The panel on the right shows the interface for stereoscopic displays.
• Figure 3: Model of human contrast sensitivity function (CSF). (A) Empirical temporal CSF model (solid lines) obtained from fitting experimental data (round markers), plotted as a function of temporal frequency for different retinal illuminances (in trolands). (B) Dependence of spatiotemporal CSF on luminance. Log contrast sensitivity is plotted as a function of temporal ($\omega$) and spatial frequency ($u$) at two luminances: 0.5cd/m2 (inner cone) and 160 cd/m2 (outer cone).
• Figure 4: Effect of stimulus speed on judder. Stimulus column: The stimulus is a bright line whose position is plotted as a function of time. The user specifies line width and speed. Input spectrum column: 2D discrete Fourier transform of the stimulus. Spatial frequency and temporal frequency are plotted on the ordinate and abscissa, respectively. Brightness represents amplitude. Output spectrum column: Spectrum filtered by the CSF. Reconstruction column: Perceived stimulus reconstructed using inverse Fourier transform of the output spectrum. Top two rows: Results for continuous (first row) and sampled stimulus (second row) with a speed of 1cm/s (1.15°/s). No motion artifacts perceived from the sampled stimulus. Bottom two rows: Results for continuous (third row) and sampled stimulus (fourth row) with a speed of 10cm/s (11.42°/s). Pixel density: 300dpi. Capture rate: 120Hz. Hold interval: 0.5. Viewing distance: 50cm.
• Figure 5: Effect of capture rate on judder. Columns in same format as Fig.\ref{['velocity']}. Top row: Results for continuous stimulus. Second row: Output from sampled stimulus with $30$Hz capture rate. Third row: Same but with capture rate of $60$Hz. Fourth row: Same but with capture rate of $120$Hz. Stimulus speed: 1cm/s (1.15°/s). Pixel density: 300dpi. Hold interval: 0.5. Viewing distance: 50cm.