A Geodesic Translation Model for Spherical Video Compression.

Spherical video coding is critical to the success of many virtual reality and related applications. This paper focuses on an important class of spherical videos whose dynamics involve camera motion. A common approach to spherical video coding is to project from the sphere onto a plane (or planes), where a standard video coder is applied. The projection induces warping resulting in complex non-linear motion in the projected domain that severely comprises the performance of motion models in standard coders. To overcome this shortcoming, we propose a new motion model that captures the motion field on the sphere, and capitalizes on insights into the perceived motion on the sphere due to camera translation. Specifically, surrounding static points are perceived as moving along their respective geodesics, which all intersect at the points where the camera velocity vector intersects the sphere. We analyze the rate of translation along geodesics and its dependence on the elevation of a pixel on the sphere with respect to the camera velocity vector. The analysis leads to a motion vector modulation scheme that perfectly captures the perceived motion of each pixel. Complementary to the new motion model, we propose a search grid tailored to capture expected geodesic motion on the sphere for effective motion estimation. The proposed method yields significant bit-rate savings over employing standard HEVC after projection, which validates its efficacy.

Effective Prediction Modes Design for Adaptive Compression With Application in Video Coding.

Adaptive prediction is an important tool for efficient compression of non-stationary signals. A common approach to achieve adaptivity is to switch between a set of prediction modes, designed to capture variations in signal statistics. The design poses several challenges including: i) catastrophic instability due to statistical mismatch driven by propagation through the prediction loop, and ii) severe non-convexity of the cost surface that is often riddled with poor local minima. Motivated by these challenges, this paper presents a near-optimal method for designing prediction modes for adaptive compression. The proposed method builds on a stable, open-loop platform, but with a subterfuge that ensures that the design is asymptotically optimized for closed-loop operation. The non-convexity is handled by deterministic annealing, a powerful optimization tool to avoid poor local minima. To demonstrate the impact of the proposed approach on practical applications, we consider adaptive, transform-domain predictor design for enhancing standard video coding. Experimental results validate the benefits of the proposed design in terms of significant performance gains for both predictive compression systems in general and video coding in particular.