Extended reality has generated great interest in recent years, among other things, due to the potential it has to transmit non-verbal communication and offer solutions to current distancing problems. This work proposes a gamified experience of three use cases that meet real-life scenarios: teleassistance, guided route, and remote technical support. The objective is to solve each of the scenario tasks that involve collaboration between local and remote users through 360-degree video communication. Specifically, 2D and 3D objects hide symbols that local users must decipher with the help of remote users, who have the necessary information to do that. In this way, the technology (360-degree video communication) and additional tools included in the system are tested: a magnifying glass to offer remote users certain areas of the scene with higher quality, visualization of the hands with the headset to make the experience more realistic, and the cartoon avatars to increase perceived social presence during the interaction. This experience considers the needs of real hybrid meeting applications that have been gamified to test 360-degree video communication but apply to other XR technologies.

The latest High Efficiency Video Coding standard (HEVC) provides a set of new coding tools to achieve a significantly higher coding efficiency than previous standards. In this standard, the pixels are first grouped into Coding Units (CU), then Prediction Units (PU), and finally Transform Units (TU). All these coding levels are organized into a quadtree-shaped arrangement that allows highly flexible data representation; however, they involve a very high computational complexity. In this paper, we propose an effective early CU depth decision algorithm to reduce the encoder complexity. Our proposal is based on a hierarchical approach, in which a hypothesis test is designed to make a decision at every CU depth, where the algorithm either produces an early termination or decides to evaluate the subsequent depth level. Moreover, the proposed method is able to adaptively estimate the parameters that define each hypothesis test, so that it adapts its behavior to the variable contents of the video sequences. The proposed method has been extensively tested, and the experimental results show that our proposal outperforms several state-of-the-art methods, achieving a significant reduction of the computational complexity (36.5% and 38.2% average reductions in coding time for two different encoder configurations) in exchange for very slight losses in coding performance (1.7% and 0.8% average bit rate increments).

The emerging high-efficiency video coding standard achieves higher coding efficiency than previous standards by virtue of a set of new coding tools such as the quadtree coding structure. In this novel structure, the pixels are organized into coding units (CU), prediction units, and transform units, the sizes of which can be optimized at every level following a tree configuration. These tools allow highly flexible data representation; however, they incur a very high computational complexity. In this paper, we propose an effective complexity control (CC) algorithm based on a hierarchical approach. An early termination condition is defined at every CU size to determine whether subsequent CU sizes should be explored. The actual encoding times are also considered to satisfy the target complexity in real time. Moreover, all parameters of the algorithm are estimated on the fly to adapt its behavior to the video content, the encoding configuration, and the target complexity over time. The experimental results prove that our proposal is able to achieve a target complexity reduction of up to 60% with respect to full exploration, with notable accuracy and limited losses in coding performance. It was compared with a state-of-the-art CC method and shown to achieve a significantly better trade-off between coding complexity and efficiency as well as higher accuracy in reaching the target complexity. Furthermore, a comparison with a state-of-the-art complexity reduction method highlights the advantages of our CC framework. Finally, we show that the proposed method performs well when the target complexity varies over time.

This paper tackles the problem of automatic brain tumor classification from Magnetic Resonance Imaging (MRI) where, traditionally, general-purpose texture and shape features extracted from the Region of Interest (tumor) have become the usual parameterization of the problem. Two main contributions are made in this context. First, a novel set of clinical-based features that intend to model intuitions and expert knowledge of physicians is suggested. Second, a system is proposed that is able to fuse multiple individual scores (based on a particular MRI sequence and a pathological indicator present in that sequence) by using a Bayesian model that produces a global system decision. This approximation provides a quite flexible solution able to handle missing data, which becomes a very likely case in a realistic scenario where the number clinical tests varies from one patient to another. Furthermore, the Bayesian model provides extra information concerning the uncertainty of the final decision. Our experimental results prove that the use of clinical-based feature leads to a significant increment of performance in terms of Area Under the Curve (AUC) when compared to a state-of-the art reference. Furthermore, the proposed Bayesian fusion model clearly outperforms other fusion schemes, especially when few diagnostic tests are available.

The motion estimation (ME) process used in the H.264/AVC reference software is based on minimizing a cost function that involves two terms (distortion and rate) that are properly balanced through a Lagrangian parameter, usually denoted as λmotion. In this paper we propose an algorithm to improve the conventional way of estimating λmotion and, consequently, the ME process. First, we show that the conventional estimation of λmotion turns out to be significantly less accurate when ME-compromising events, which make the ME process to perform poorly, happen. Second, with the aim of improving the coding efficiency in these cases, an efficient algorithm is proposed that allows the encoder to choose between three different values of λmotion for the Inter 16x16 partition size. To be more precise, for this partition size, the proposed algorithm allows the encoder to additionally test λmotion=0 and λmotion arbitrarily large, which corresponds to minimum distortion and minimum rate solutions, respectively. By testing these two extreme values, the algorithm avoids making large ME errors. The experimental results on video segments exhibiting this type of ME-compromising events reveal an average rate reduction of 2.20% for the same coding quality with respect to the JM15.1 reference software of H.264/AVC. The algorithm has been also tested in comparison with a state-of-the-art algorithm called context adaptive Lagrange multiplier. Additionally, two illustrative examples of the subjective performance improvement are provided.

Flicker is a common video-compression-related temporal artifact. It occurs when co-located regions of consecutive frames are not encoded in a consistent manner, especially when Intra frames are periodically inserted at low and medium bit rates. In this paper we propose a flicker reduction method which aims to make the luminance changes between pixels in the same area of consecutive frames less noticeable. To this end, a temporal low-pass filtering is proposed that smooths these luminance changes on a block-by-block basis. The proposed method has some advantages compared to another state-of-the-art methods. It has been designed to be compliant with conventional video coding standards, i.e., to generate a bitstream that is decodable by any standard decoder implementation. The filter strength is estimated on-the-fly to limit the PSNR loss and thus the appearance of a noticeable blurring effect. The proposed method has been implemented on the H.264/AVC reference software and thoroughly assessed in comparison to a couple of state-of-the-art methods. The flicker reduction achieved by the proposed method (calculated using an objective measurement) is notably higher than that of compared methods: 18.78% versus 5.32% and 31.96% versus 8.34%, in exchange of some slight losses in terms of coding efficiency. In terms of subjective quality, the proposed method is perceived more than two times better than the compared methods.

Flicker is a common video coding artifact that occurs especially at low and medium bit rates. In this paper we propose a temporal filter-based method to reduce flicker. The proposed method has been designed to be compliant with conventional video coding standards, i.e., to generate a bitstream that is decodable by any standard decoder implementation. The aim of the proposed method is to make the luminance changes between consecutive frames smoother on a block-by-block basis. To this end, a selective temporal low-pass filtering is proposed that smooths these luminance changes on flicker-prone blocks. Furthermore, since the low-pass filtering can incur in a noticeable blurring effect, an adaptive algorithm that allows for limiting the PSNR loss -and thus the blur- has also been designed. The proposed method has been extensively assessed on the reference software of the H.264/AVC video coding standard and compared to a state-of-the-art method. The experimental results show the effectiveness of the proposed method and prove that its performance is superior to that of the state-of-the-art method.

The latest H.264/AVC video coding standard achieves high compression rates in exchange for high computational complexity. Nowadays, however, many application scenarios require the encoder to meet some complexity constraints. This paper proposes a novel complexity control method that relies on a hypothesis testing that can handle time-variant content and target complexities. Specifically, it is based on a binary hypothesis testing that decides, on a macroblock basis, whether to use a low- or a high-complexity coding model. Gaussian statistics are assumed so that the probability density functions involved in the hypothesis testing can be easily adapted. The decision threshold is also adapted according to the deviation between the actual and the target complexities. The proposed method is implemented on the H.264/AVC reference software JM10.2 and compared with a state-of-the-art method. Our experimental results prove that the proposed method achieves a better trade-off between complexity control and coding efficiency. Furthermore, it leads to a lower deviation from the target complexity.

The H.264/AVC standard achieves a high coding efficiency compared to previous standards. However, this gain is accomplished at great computational cost, with mode decision being one of the most demanding subsystems. In this paper, a two-level classification-based approach to the inter mode decision problem is proposed. A first classifier detects SKIP/Direct modes, while a second one is able to decide whether to use a large (16 × 16, 16 × 8, and 8 × 16) or a small mode (8 × 8, 8 × 4, 4 × 8, and 4 × 4). The suggested classifiers are binary and linear, and the input features in the classifiers have been carefully selected. A novel cost function that pays more attention to the most critical samples during the classifier training process has been designed. The experimental results show an average computational savings of 60% of the total encoding time with respect to JM10.2 over a comprehensive variety of sequences and formats. This is achieved with negligible degradation in rate-distortion performance and compares favorably with state-of-the-art fast mode decision methods. Furthermore, the proposed method has been successfully assessed at different levels of complexity reduction.

The H.264/AVC standard achieves a high coding efficiency compared to previous standards, but in exchange for a very high computational cost. This paper focuses on the mode decision subsystem, which is a critical one from the computational cost point of view. In particular, a hierarchical early-termination-based approach to the inter mode decision problem is proposed, in such a way that a cascade of early stops are considered, going from the most likely modes to the less likely ones. The main novelty of the proposed algorithm relies on the use of a regionalized hypothesis testing methodology for making the decisions on every potential early stop. The proposed method has been assessed on a large set of QCIF video sequences for several qualities, achieving a quite notable improvement in terms of total encoding time reduction in relation to a well-established mode decision method. Specifically, the time reduction is 43.43 % and 21.33 % for IPPP and IBBP patterns, respectively, without any significant quality loss.