We present a method to compress geometry information of point clouds that explores redundancies across consecutive frames of a sequence. It uses octrees and works by progressively increasing resolution of the octree. At each branch of the tree, we generate an approximation of the child nodes by a number of methods which are used as contexts to drive an arithmetic coder. The best approximation, i.e. the context that yields the least amount of encoding bits, is selected and the chosen method is indicated as side information for replication at the decoder. The core of our method is a context-based arithmetic coder in which a reference octree is used as reference to encode the current octree, thus providing 255 contexts for each output octet. The 255×255 frequency histogram is viewed as a discrete 3D surface and is conveyed to the decoder using another octree. We present two methods to generate the predictions (contexts) which use adjacent frames in the sequence (inter-frame) and one method that works purely intra-frame. The encoder continuously switches the best mode among the three and conveys such information to the decoder. Since an intra-frame prediction is present, our coder can also work in purely intra-frame mode, as well. Extensive results are presented to show the method's potential against many compression alternatives for the geometry information in dynamic voxelized point clouds.

We propose a mixed-resolution point-cloud representation and an example-based super-resolution framework, from which several processing tools can be derived, such as compression, denoising and error concealment. By inferring the high-frequency content of low-resolution frames based on the similarities between adjacent full-resolution frames, the proposed framework achieves an average 1.18 dB gain over lowpass versions of the point-cloud, for a projection-based distortion metric [1], [2].

Energy consumption has become a leading design constraint for computing devices in order to defray electric bills for individuals and businesses. Over the past years, digital video communication technologies have demanded higher computing power availability and, therefore, higher energy expenditure. In order to meet the challenge to provide software-based video encoding solutions, we adopted an open source software implementation of an H.264 video encoder, the x264 encoder, and optimized its prediction stage in the energy sense (E). Thus, besides looking for the coding options which lead to the best coded representation in terms of rate and distortion (RD), we constrain the process to fit within a certain energy budget. i.e., an RDE optimization. We considered energy as the time integration of the real demanded electric power for a given system. We present an RDE-optimized framework which allows for software-based real-time video compression, meeting the desired targets of electrical consumption, hence, controlling carbon emissions. We show results of energy-constrained compression wherein one can save as much as 35% of the energy with small impact on RD performance.

Encoding video sequences is a computation-demanding task in high-performance codecs. Optimizing this stage may result in a substantial encoding speed-up. In this paper, we propose a faster approach to encode sequences with the H.264/AVC codec, using mixed-resolution. By having some of the frames down-sampled, the overall computation is reduced, without greatly affecting the rate-distortion performance, which remains evaluated using the sequence's original resolution. In addition to this approach, a method which regards only the most frequent prediction modes of a frame is incorporated, so that the combined complexity reduction can be significant with negligible performance losses.

In order to enable real-time software-based video encoding, in this work we optimized the prediction stage of an H.264 video encoder, in the complexity sense. Thus, besides looking for the coding options which lead to the best coded representation in terms of rate and distortion (RD), we constrain to a complexity (C) budget. We present a complexity optimized framework (RDC-optimized) which allows for real-time video compression and that does not make use of frame-skipping to comply to the desired encoding speed. We developed our framework around an open source software implementation of the H.264/AVC, the the ×264 encoder. Results show that tight complexity control is attainable in practice, with very little loss in RD performance.

Real-time high-definition video encoding is a computation-hungry task that challenges software-based solutions. For that, in this work we adopted an Intel software implementation of an H.264 video encoder and optimized its prediction stage in the complexity sense (C). Thus, besides looking for the coding options which lead to the best coded representation in terms of rate and distortion, we constrain the process to fit within a certain time budget. We present an RDC-optimized framework which allows for real-time HD video compression.

We propose a framework for complexity scalability in H.264. The prediction is constrained so that only a subset of prediction modes are tested. The test subset is found by ranking the most ¿popular¿ modes (those the are most often picked as best) and selecting the modes that maximize their expected occurrence frequency given a complexity constraint. Ranking is performed by selecting a small set of macroblocks (the sampling population), for which all modes are tested. The remaining macroblocks only test the available dominant modes. The modes in the sampling population of a frame are used to process the next frame. Results are shown to verify the performance of the proposed method, which reveals sizeable complexity savings at small penalties.

Video compression is a computation-intensive task and real-time implementation of state-of-the-art video standards is a challenge. In this paper we propose a complexity scalable framework to H.264/AVC prediction module since the “intra” and “inter” prediction steps are responsible for almost all computation complexity. The idea is to employ a subset of prediction modes instead of testing all modes recommended by H.264/AVC standard. Only the most “popular” prediction modes are ranked and tested until reaching a complexity budget. The ranking is done by sampling macroblocks of previous frames. Results show a negligible the quality loss while achieving large computational savings.