A novel image approximation framework called Steered Mixture-of-Experts (SMoE) was recently presented. SMoE has multiple applications in coding, scale-conversion, and general processing of image modalities. In particular, it has strong potential for coding and streaming higher dimensional image modalities that are necessary to leverage full translational and rotational freedom (6 Degrees-of-Freedom) in virtual reality for camera captured images. In this paper, we analyze the rendering performance of SMoE for 2D images and 4D light fields. Two different GPU implementations that parallelize the SMoE regression step at pixel-level are presented, including experimental evaluations based on rendering performance and quality. In this paper it is shown that on appropriate hardware, the OpenCL implementation can achieve 85fps and 22fps for respectively 1080p and 4K renderings of large models with more than 100.000 of Gaussian kernels.

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Steered Mixture-of-Experts (SMoE) is a novel framework for the approximation, coding, and description of image modalities such as light field images and video. The future goal is to arrive at a representation for Six Degrees-of-Freedom (6DoF) image data. Previous research has shown the feasibility of real-time pixel-parallel rendering of static light field images. Each pixel is independently reconstructed by kernels that lay in its vicinity. The number of kernels involved forms the bottleneck on the achievable framerate. The goal of this paper is twofold. Firstly, we introduce pixel-level rendering of light field video, as previous work only rendered static content. Secondly, we investigate rendering using a predefined number of most significant kernels. As such, we can deliver hard real-time constraints by trading off the reconstruction quality.

Steered Mixture-of-Experts (SMoE) is a novel framework for representing multidimensional image modalities. In this paper, we propose a coding methodology for SMoE models that is readily extendable to any dimensional SMoE model, thus representing any image modality of any dimension. We evaluate the coding performance of SMoE models of light field video, a 5D image modality, i.e. time, two angular, and two spatial dimensions. The coding consists of the exploiting the redundancy between the parameters of SMoE models, i.e. a set of multivariate Gaussian distributions. We compare the performance of three multi-view HEVC (MV-HEVC) configurations that differ in terms of random access. Each subaperture view from the light field video is interpreted as a single view in MV-HEVC. Experiments validate that excellent coding performance compared to MV-HEVC for low-to midrange bitrates in terms of PSNR and SSIM with bitrate savings up to 75%.