Sorry, you need to enable JavaScript to visit this website.

The Steered Mixture-of-Experts (SMoE) framework targets a sparse space-continuous representation for images, videos, and light fields enabling processing tasks such as approximation, denoising, and coding.
The underlying stochastic processes are represented by a Gaussian Mixture Model, traditionally trained by the Expectation-Maximization (EM) algorithm.
We instead propose to use the MSE of the regressed imagery for a Gradient Descent optimization as primary training objective.

We propose an efficient coding scheme for a dense light field, i.e.,
a set of multi-viewpoint images taken with very small viewpoint intervals.
The key idea behind our proposal is that a light field is represented
only using weighted binary images, where several binary
images and corresponding weight values are to be chosen to optimally
approximate the light field. The coding scheme derived from
this idea is completely different from those of modern image/video
coding standards. However, we found that our scheme can achieve

Light fields aim to represent visual information in 3D space. They are 4D structures that contain the images of a given scene from a sampled 2D range of viewpoints. When acquired using a lenslet camera, in addition to the ordinary intra-view redundancy, these views have a great deal of inter-view redundancy. In this work we propose a light field codec that fully exploits the 4D redundancy of light fields by using a 4D transform and hexadeca-trees. It initially divides the light field into 4D blocks and computes a 4D Discrete Cosine Transform of each one.

Conventional video coding approaches follow a hybrid motion prediction / residual transform coding paradigm, which limits the discovery of redundancy to individual pairs of video frames.
On the other hand, computer vision techniques like structure-from-motion (SfM) have long exploited redundancy across a large group of frames to estimate a rigid 3D object structure.
In this paper, leveraging on previous SfM techniques, we construct a rate-distortion (RD) optimized 3D planar model from a target spatial region in a frame group as a unified signal predictor for these frames.

In this work we study the 4D sparsity of light fields using as main tool the 4D-Discrete Cosine Transform. We analyze the two JPEG Pleno light field datasets, namely the lenslet-based and the High- Density Camera Array (HDCA) datasets. The results suggest that the lenslets datasets exhibit a high 4D redundancy, with a larger inter-view sparsity than the intra-view one. For the HDCA datasets, there is also 4D redundancy worthy to be exploited, yet in a smaller degree. Unlike the lenslets case, the intra-view redundancy is much larger than the inter-view one.

This work presents a thresholding method for processing the predicted samples in the state-of-the-art High Efficiency Video Coding (HEVC) standard. The method applies an integer-based approximation of the discrete cosine transform to an extended prediction block and sets transform coefficients beneath a certain threshold to zero. Transforming back into the sample domain yields the improved prediction signal. The method is incorporated into a software implementation that is conforming to the HEVC standard and applies to both intra and inter predictions.