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Consumer electronics have started to support 8K/4K/UHD/SUHD videos in order to improve the perceived QoE. It is beneficial to assess the consumer’s perceived quality degradation at the output prior to transmitting the video in an error prone channel, to include concealment support and error resilient features to minimize these artifacts. While existing error resilient schemes have attempted to include source coding error and the channel’s error propagation, they have ignored the error resilience and concealment accuracy at the decoder when encoding the video which allows for end-to-end error capture and mitigation to improve consumer QoE. This paper proposes a two-pass codec encoding scheme that determines the motion vectors and the coding modes which not only mitigate the source coding error and the channel error propagation, but also improve the accuracy of the error concealment operation. The proposed method incorporates an end-to-end probabilistic distortion model and utilizes the concealment error in the subsequently encoding frames while estimating the motion vectors for the current frame. The experiment achieves up to 1.47dB PSNR gain for diverse video contents in comparison to the state-of-the-art techniques at a 10% packet error rate. This objective gain was also supported by the user perceived QoE.

Consumer Electronics (CE) devices have started to support 360° videos in order to improve the perceived video quality by consumers. Higher compression in 360° videos not only lead to improved consumer perception, but also efficient energy and storage usage in CE devices. Consequently, efficient compression of such video content is becoming a critical design consideration of CE devices. To this end, this paper first proposes a residual weighting algorithm that utilizes the features of Weighted Craster Parabolic Projection Peak Signal-to-Noise Ratio (WCPPPSNR), to instigate the first stage of magnitude reduction of the residuals. The model parameters associated with the residual weighting algorithm are determined in the initial experimental stage. Next, the paper proposes a Quantization Parameter (QP) optimization technique which introduces the second stage of magnitude reduction for residuals with minimal impact on the overall video quality. The proposed method is tested on the Versatile Video Coding (VVC) test model with All-Intra configuration. In this context, the empirical results demonstrate that the proposed method improves the coding efficiency by 3.34% on average and outperforms the state-of-the-art techniques.

Inter-frame prediction in HEVC uses two types of reference pictures: short-term and long-term. Out of these, long-term reference (LTR) pictures enable exploiting correlation among frames with extended temporal distances. In addition, LTR pictures improve the inter-frame prediction where video scenes are repeated such as in TV-series episodes, news broadcasts and movies. In this context, this paper proposes an algorithm to calculate LTR pictures using artificially generated virtual reference frames for static-camera scenes. The experimental results demonstrate an average coding improvement of 2.34% in terms of Bjontegaard Delta Bit Rate(BDBR), when compared with the HEVC reference encoder HM16.8.

High Efficiency Video Coding(HEVC) employs both past or future frames when encoding the current frame in a video sequence. This paper proposes a framework for using virtual reference frames, to achieve increased coding gains in the long-term for repetitive scenes in static camera scenarios.

The brute force rate-distortion optimisation based approach used in the High Efficiency Video Coding(HEVC) encoders to determine the best block partitioning structure for a given content demands an excessive amount of computational resources. In this context, this paper proposes a novel algorithm to reduce the computational complexity of HEVC inter-prediction using Support Vector Machines. The proposed algorithm predicts the Coding Unit (CU) split decision of a particular block enabling the encoder to directly encode the selected block, avoiding the unnecessary evaluation of the remaining CU size combinations. Experimental results demonstrate encoding time reductions of ∼58% ∼50%2.27%1.89% Bjontegaard Delta Bit Rate (BDBR) losses for Random Access and Low-Delay B configurations, respectively.

This paper proposes a content adaptive fast CU size selection algorithm for HEVC intra-prediction using weighted support vector machines. The proposed algorithm demonstrates an average encoding time reduction of 52.38% with 1.19% average BDBR increase compared to HM16.1 reference encoder.

The use of machine learning techniques for encoding complexity reduction in recent video coding standards such as High Efficiency Video Coding (HEVC) has received prominent attention in the recent past. Yet, the dynamically changing nature of the video contents makes it evermore challenging to use rigid traditional inference models for predicting the encoding decisions for a given content. In this context, this paper investigates the resulting implications on the coding efficiency and the encoding complexity, when using offline trained and online trained machine-learning models for coding unit size selection in the HEVC intra-prediction. The experimental results demonstrate that the ground truth encoding statistics of the content being encoded, is crucial to the efficient encoding decision prediction when using machine learning based prediction models.

Even though the latest video compression techniques such as High Efficiency Video coding (HEVC) have succeeded in significantly alleviating the bandwidth consumption during high resolution video transmission, they have become severely susceptible to transmission errors. Overcoming the resulting temporal impact of the transmission errors on the decoded video requires efficient error resilient schemes that can introduce robustness features to the coded video in order to mitigate the negative impact on the viewer. To this end, this paper proposes a rate-controlled error resilient bit allocation scheme, together with an encoding parameter selection process, to adaptively determine the most robust video coding parameters and the decoder error concealment operations during the encoding itself. Consequently, the proposed method has demonstrated 0.48dB-0.62dB PSNR gain over the state-of-the art methods at the same bit rate.

Along with the rapid increase in the availability of high-quality video formats such as high definition (HD), ultra HD, and high dynamic range, a huge demand for data rates during their transmission has become inevitable. Consequently, the role of video compression techniques has become crucially important in the process of mitigating the data rate requirements. Even though the latest video codec high efficiency video coding (HEVC) has succeeded in significantly reducing the data rate compared to its immediate predecessor H.264/advanced video coding, the HEVC coded videos in the meantime have become even more vulnerable to network impairments. Therefore, it is equally important to assess the consumers’ perceived quality degradation prior to transmitting HEVC coded videos over an error-prone network, and to include error resilient features so as to minimize the adverse effects of those impairments. To this end, this paper proposes a probabilistic model which accurately predicts the overall distortion of the decoded video at the encoder followed by an accurate QP– $\lambda $ relationship which can be used in the rate-distortion optimization (RDO) process. During the derivation process of the probabilistic model, the impacts from the motion vectors, the pixels in the reference frames, and the clipping operations are accounted, and consequently, the model is capable of minimizing the prediction error to as low as 3.11%, whereas the state-of-the-art methods cannot reach below 20.08%, under identical conditions. Furthermore, the enhanced RDO process has resulted in 21.41%–43.59% improvement in the BD-rate compared to the state-of-the-art error resilient algorithms.

Error concealment techniques such as motion copying require significant changes to HEVC (High Efficiency Video Coding) motion estimation process when incorporated in error resilience frameworks. This paper demonstrates a novel motion estimation mechanism incorporating the concealment impact from future coding frames to achieve an average 0.73 dB gain over the state-of-the-art.

Social interaction of groups of users, amongst themselves and with the media content itself, is increasingly becoming popular due to the advancements in the Internet access technologies. However, multimedia resource provisioning for dispersed user groups poses a challenge and demands innovative technologies. This paper proposes a novel approach based on Particle Swarm Optimization (PSO) to optimally allocate computational and networking resources to a group of interactive users, such that the group Quality-of-Service (QoS) is maximized. We evaluate the performance of the proposed improved PSO method with respect to the state-of-the-art greedy resource allocation mechanisms and related PSO approaches. The ability to find a feasible solution (i.e., the serving probability) and the accuracy of such solutions are compared for different network topologies. The proposed method demonstrates reduced computational complexity, an up to 40% increase in the serving probability compared to the greedy methods, and up to 60 times faster convergence compared to the basic PSO approach. Overall, the comparable QoS level to the optimal solution suggests that the proposed solution efficiently allocates the resources available in the network.

Though ubiquitous, the full potential of consumer electronic devices in the home, as content creators, remains underutilized due to the limited interaction between the consumers and the existing on-demand application and media services. Although services such as interactive television could change this, the geographic distribution of groups of consumers and the need for on-the-fly media processing that this entails, makes the efficient utilization of resources a complex optimization task requiring mechanisms to simultaneously allocate processing and network resources to groups of users. However, these technologies have not yet been developed, and brute force methods remain prohibitively complex. In order to overcome this problem, this paper proposes heuristic algorithms to both generate end-to-end delay bound multicast trees for individual groups of users and to co-locate multiple multicast trees, such that a minimum group quality metric can be satisfied. The performance of the proposed heuristic solution is evaluated in terms of the serving probability, i.e., the resource utilization efficiency, and computation time of the resource allocation decision making process. Simulation results show that improvements in the serving probability of up to 50%, in comparison with existing generic resource allocation schemes, and several orders of magnitude reduction of the computation time, in comparison to an optimal linear programming solution approach, can be achieved.