It is known that humans can be insensitive to large changes in illumination. For example, if an object of interest is extracted from one digital photograph and inserted into another, we do not always notice the differences in illumination between the object and its new background. This inability to spot illumination inconsistencies is often the key to success in digital “doctoring” operations. We present a set of experiments in which we explore the perception of illumination in outdoor scenes. Our results can be used to predict when and why inconsistencies go unnoticed. Applications of the knowledge gained from our studies include smarter digital “cut-and-paste” and digital “fake” detection tools, and image-based composite scene backgrounds for layout and previsualization.

Motion blur effects are commonly used in racing games [Sousa 2008; Vlachos 2008; Ritchie et al. 2010] to add a sense of realism as well as to minimize artifacts due to strobing and temporal aliasing [Glassner 1999]. Typically, motion blur computations are expensive, and for real-time applications, trade-offs are made between the quality of the effects and the computational cost. In this work, we wanted to understand: (i) the practical impact of the motion blur effect on the player experience; and (ii) whether the value gained by including the effect is worth the extra cost in computation, real-time performance, development time, etc. We studied the objective and subjective aspects of the player experience for Split Second: Velocity (Black Rock Studios, Disney), a high-speed racing game, in the presence and absence of the motion blur effect. We found that neither objective measures of participants' performance (e.g., time to complete a race) nor subjective measures of the player experience (e.g, enjoyment of a race, perceived speed) were affected, even though participants could reliably detect the presence of the motion blur effect. We conclude that motion blur effects, while useful for reducing artifacts and achieving a realistic 'look', do not significantly enhance the player experience.

We are interested in identifying the material category, e.g. glass, metal, fabric, plastic or wood, from a single image of a surface. Unlike other visual recognition tasks in computer vision, it is difficult to find good, reliable features that can tell material categories apart. Our strategy is to use a rich set of low and mid-level features that capture various aspects of material appearance. We propose an augmented Latent Dirichlet Allocation (aLDA) model to combine these features under a Bayesian generative framework and learn an optimal combination of features. Experimental results show that our system performs material recognition reasonably well on a challenging material database, outperforming state-of-the-art material/texture recognition systems.

We consume video content in a multitude of ways, including in movie theaters, on television, on DVDs and Blu-rays, online, on smart phones, and on portable media players. For quality control purposes, it is important to have a uniform viewing experience across these various platforms. In this work, we focus on voice synchronization, an aspect of video quality that is strongly affected by current post-production and transmission practices. We examined the synchronization of an actor's voice and lip movements in two distinct scenarios. First, we simulated the temporal mismatch between the audio and video tracks that can occur during dubbing or during broadcast. Next, we recreated the pitch changes that result from conversions between formats with different frame rates. We show, for the first time, that these audio visual mismatches affect viewer enjoyment. When temporal synchronization is noticeably absent, there is a decrease in the perceived performance quality and the perceived emotional intensity of a performance. For pitch changes, we find that higher pitch voices are not preferred, especially for male actors. Based on our findings, we advise that mismatched audio and video signals negatively affect viewer experience.

High dynamic range (HDR) imaging is an area of increasing importance, but most display devices still have limited dynamic range (LDR). Various techniques have been proposed for compressing the dynamic range while retaining important visual information. Multi-scale image processing techniques, which are widely used for many image processing tasks, have a reputation of causing halo artifacts when used for range compression. However, we demonstrate that they can work when properly implemented. We use a symmetrical analysis-synthesis filter bank, and apply local gain control to the subbands. We also show that the technique can be adapted for the related problem of "companding", in which an HDR image is converted to an LDR image, and later expanded back to high dynamic range.