The geometry of a point cloud is commonly represented by an octree recursively decomposing a 3D volume into eight child sub-volumes. Said volumes and sub-volumes are associated with nodes and child-nodes of the octree. The geometry is defined by the occupancy information indicating the presence or not of a point in each of the sub-volumes. This naturally leads to an eight-bit occupancy information to be coded for each internal node of the tree.

Compression of dynamic point clouds for streaming and broadcast is a big challenge arising in mixed and virtual reality applications. To advance this area, common performance benchmarking and assessment is key as to compare different technologies consistently. This paper presents a methodology for performance assessment that was established in the MPEG point cloud compression activity in a collaboration of leading companies and academic institutes. It includes selected datasets, objective metrics and target coding rates corresponding to distinguishable subjective qualities and different use cases. The performance assessment methodology is applied to a compression framework on a large dataset developing an anchor performance benchmark. The benchmark results and methodology are used in the recent MPEG Call for proposals on point cloud coding to consistently compare and evaluate technologies.

A new video compression scheme for High Dynamic Range (HDR) contents is presented. It is assumed that both Standard Dynamic Range (SDR) and HDR devices coexist in the ecosystem, and the scheme is constructed to ensure backward compatibility such that a single video stream carries both the original HDR video and an automatically generated SDR version of this video without noticeable overhead relatively to the distribution of the HDR video only. Compression performance are shown to be solidly improved compared to the conservative and non-backward-compatible approach using the HEVC Main10 codec and the Perceptual Quantizer (PQ) Electro-Optical Transfer Function (OETF) as non-linearity.

Block based video codecs developed in last decades have employed Discrete Cosine Transform (DCT) as a candidate transform for compacting the energy of the predicted residual data into fewer coefficients. However, DCT is not optimal for blocks with directional structure and high texture. Content-adaptive transforms can be used to achieve better compression. However, these adaptive transforms must be sent to the decoder, increasing the bit-rate in return. It is therefore important to take care of the overhead of sending the transforms in order to benefit from the better compaction of the adaptive transforms. This paper addresses this issue and provides methods to efficiently encode these adaptive transforms that are learned from the content. A statistical model is derived to determine the precision required to encode a transform with a negligible loss in the performance of adaptive transforms. Results shows that the overhead can be reduced by an average 70% for low QP and 86% for high QP values compared to encoding the complete transform with a little performance loss.

Most of the recent video compression standards employ the Discrete Cosine Transform (DCT) for transforming the residual signal in order to remove spatial correlation and to achieve higher compression efficiency. However, by careful adaptation of transforms to the video content, a better set of integer transforms can be obtained. This paper proposes a new on-the-fly block-based transform optimization technique which involves first the classification of the residual blocks based on the cost of encoding the block, and then the generation of new optimized transforms for each class. An annealing based learning technique is further proposed in this paper in order to improve the performance of the optimization algorithm. The algorithm is tested using the latest HEVC test software where an optimized set of transforms is learned on the first frame of the HEVC test sequences and then applied to the subsequent frames in a Random Access (RA) and All Intra (AI) configuration. The results shows that this method can gain over 2% in terms of Bjontegaard Delta (BD)-rate compared to standard HEVC encoder in AI configuration and nearly 1.5% in RA.

Adaptive transform learning schemes have been extensively studied in the literature with a goal to achieve better compression efficiency compared to extensively used Discrete Cosine Transforms (DCT) inside a video codec. These transforms are learned offline on a large training set and are tested either in competition with or in place of the core transforms i.e. DCT. In our previous work, we proposed an alternative approach where a set of non-separable content-adaptive transforms are learned on-the-fly on a sequence and then tested on the same sequence in competition with the core transforms. In this paper, we propose to further improve the previously proposed learning scheme by improving the coding of transformed coefficients in context to the online learning of transforms. The first proposed method improves the convergence of the learning scheme by re-ordering the transformed coefficients at each iteration. The second proposed method improves the compression efficiency by modifying the coding of last significant coefficient position in context to adaptive transforms. The results shows that by combining the above proposed methods, one can achieve 1.2% gain on top of the previously proposed scheme.

This paper presents a new approach for a scalable extension of High Efficiency Video Coding (HEVC) intra pictures. The proposed scalable intra codec targets very low complexity together with coding efficiency, and thus employs only one prediction mode, known as inter layer intra prediction. The main novelty of the codec is a new fast rate-distortion optimization approach, which is founded on the rate-distortion theory. It exploits precomputed offline information for the reduction of the computational complexity, and a new allocation process for the balancing of the coded information. The precomputed information is a set of optimal quantifiers and associated rate-distortion curves for predetermined statistical models that are used for efficiently coding the Discrete Cosine Transform channels. This low-complexity intra coding tool has been proposed to the scalable HEVC call for proposals. In the scalable standard testing conditions, the proposed intra codec shows compression performance with a bit-rate increase of around 10% only compared with the HEVC single-layer encoder.

We present a process to automatically generate three-dimensional mesh representations of the complex, arborized cell membrane surface of cortical neurons (the principal information processing cells of the brain) from nonuniform morphological measurements. Starting from manually sampled morphological points (3D points and diameters) from neurons in a brain slice preparation, we construct a polygonal mesh representation that realistically represents the continuous membrane surface, closely matching the original experimental data. A mapping between the original morphological points and the newly generated mesh enables simulations of electrophysiolgical activity to be visualized on this new membrane representation. We compare the new mesh representation with the state of the art and present a series of use cases and applications of this technique to visualize simulations of single neurons and networks of multiple neurons.