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The application of machine learning to video coding is generally studied in two main approaches: end-to-end video coding using deep neural networks and classic hybrid codecs with individual tools implemented using such networks. This work exploits the latter approach, where a trained Artificial Neural Network (ANN) is used for fast implementation of the search for the partitioning of Coding Tree Units (CTU) into Coding Units (CUs) and Prediction Units (PUs). The proposed approach differs from the previous ones, among other factors, by the application of an ANN with probabilistic soft outputs, which allows for assessing the probability of particular division patterns. This is followed by a decision algorithm that selects more than one candidate division pattern from the most probable patterns provided by ANN. Two variants of the ANN model are considered, out of which the extended one involves deep partitioning at the PU-level. The number of selected division pattern candidates is controlled by a single parameter – uncertainty range – that allows for choosing a trade-off between the complexity and the loss of coding efficiency. This feature is particularly important for broadcasting applications, where processing must be adjusted to the current computational load and resource availability. The experiments demonstrate that the proposed approach yields good results using much smaller ANNs than those described in the references. This makes the proposed approach well-suited for using on edge servers where GPU may not be available. The source code for the respective HEVC codec software implementation is provided.

Hybrid video compression plays an invaluable role in digital video transmission and storage services and systems. It performs several-hundred-fold reduction in the amount of video data, which makes these systems much more efficient. An important element of hybrid video compression is entropy coding of the data. The state-of-the-art in this field is the newest variant of the Context-based Adaptive Binary Arithmetic Coding entropy compression algorithm which recently became part of the new Versatile Video Coding technology. This work is a part of research that is currently underway to further improve the Context-based Adaptive Binary Arithmetic Coding technique. This paper provides analysis of the potential for further improvement of the Context-based Adaptive Binary Arithmetic Coding technique by more accurate calculation of probabilities of data symbols. The mentioned technique calculates those probabilities by the use of idea of the two-parameters hypothesis. For the needs of analysis presented in this paper, an extension of the aforementioned idea was proposed which consists of three- and four-parameters hypothesis. In addition, the paper shows the importance of proper calibration of parameter values of the method on efficiency of data compression. Results of experiments show that for the considered in the paper variants of the algorithm improvement the possible efficiency gain is at levels 0.11% and 0.167%, for the three- and four-parameter hypothesis, respectively.

This paper introduces an end-to-end learned image compression system, termed ANFIC, based on Augmented Normalizing Flows (ANF). ANF is a new type of flow model, which stacks multiple variational autoencoders (VAE) for greater model expressiveness. The VAE-based image compression has gone mainstream, showing promising compression performance. Our work presents the first attempt to leverage VAE-based compression in a flow-based framework. ANFIC advances further compression efficiency by stacking and extending hierarchically multiple VAE’s. The invertibility of ANF, together with our training strategies, enables ANFIC to support a wide range of quality levels without changing the encoding and decoding networks. Extensive experimental results show that in terms of PSNR-RGB, ANFIC performs comparably to or better than the state-of-the-art learned image compression. Moreover, it performs close to VVC intra coding, from low-rate compression up to perceptually lossless compression. In particular, ANFIC achieves the state-of-the-art performance, when extended with conditional convolution for variable rate compression with a single model. The source code of ANFIC can be found at https://github.com/dororojames/ANFIC.