Extended Signaling Methods for Reduced Video Decoder Power Consumption Using Green Metadata

In this paper, we discuss one aspect of the latest MPEG standard edition on energy-efficient media consumption, also known as Green Metadata (ISO/IEC 232001-11), which is the interactive signaling for remote decoder-power reduction for peer-to-peer video conferencing. In this scenario, the receiver of a video, e.g., a battery-driven portable device, can send a dedicated request to the sender which asks for a video bitstream representation that is less complex to decode and process. Consequently, the receiver saves energy and extends operating times. We provide an overview on latest studies from the literature dealing with energy-saving aspects, which motivate the extension of the legacy Green Metadata standard. Furthermore, we explain the newly introduced syntax elements and verify their effectiveness by performing dedicated experiments. We show that the integration of these syntax elements can lead to dynamic energy savings of up to 90% for software video decoding and 80% for hardware video decoding, respectively.Comment: 5 pages, 2 figure

Two-Stage Overfitting of Neural Network-Based Video Coding In-Loop Filter

Modern video coding standards like the Versatile Video Coding (VVC) produce compression artefacts, due to their block-based, lossy compression techniques. These artefacts are mitigated to an extent by the in-loop filters inside the coding process. Neural Network (NN) based in-loop filters are being explored for the denoising tasks, and in recent studies, these NN-based loop filters are overfitted on test content to achieve a content-adaptive nature, and further enhance the visual quality of the video frames, while balancing the trade-off between quality and bitrate. This loop filter is a relatively low-complexity Convolutional Neural Network (CNN) that is pretrained on a general video dataset and then fine-tuned on the video that needs to be encoded. Only a set of parameters inside the CNN architecture, named multipliers, are fine-tuned, thus the bitrate overhead, that is signalled to the decoder, is minimized. The created weight update is compressed using the Neural Network Compression and Representation (NNR) standard. In this project, an exploration of high-performing hyperparameters was conducted, and the two-stage training process was employed to, potentially, further increase the coding efficiency of the in-loop filter. A first-stage model was overfitted on the test video sequence, it explored on which patches of the dataset it could improve the quality of the unfiltered video data, and then the second-stage model was overfitted only on these patches that provided a gain. The model with best-found hyperparameters saved on average 1.01% (Y), 4.28% (Cb), and 3.61% (Cr) Bjontegaard Delta rate (BD-rate) compared to the Versatile Video Coding (VVC) Test Model (VTM) 11.0 NN-based Video Coding (NNVC) 5.0, Random Access (RA) Common Test Conditions (CTC). The second-stage model, although exceeded the VTM, it underperformed with about 0.20% (Y), 0.23% (Cb), and 0.18% (Cr) BD-rate with regards to the first-stage model, due to the high bitrate overhead created by the second-stage model

End to end Multi-Objective Optimisation of H.264 and HEVC Codecs

All multimedia devices now incorporate video CODECs that comply with international video coding standards such as H.264 / MPEG4-AVC and the new High Efficiency Video Coding Standard (HEVC) otherwise known as H.265. Although the standard CODECs have been designed to include algorithms with optimal efficiency, large number of coding parameters can be used to fine tune their operation, within known constraints of for e.g., available computational power, bandwidth, consumer QoS requirements, etc. With large number of such parameters involved, determining which parameters will play a significant role in providing optimal quality of service within given constraints is a further challenge that needs to be met. Further how to select the values of the significant parameters so that the CODEC performs optimally under the given constraints is a further important question to be answered. This thesis proposes a framework that uses machine learning algorithms to model the performance of a video CODEC based on the significant coding parameters. Means of modelling both the Encoder and Decoder performance is proposed. We define objective functions that can be used to model the performance related properties of a CODEC, i.e., video quality, bit-rate and CPU time. We show that these objective functions can be practically utilised in video Encoder/Decoder designs, in particular in their performance optimisation within given operational and practical constraints. A Multi-objective Optimisation framework based on Genetic Algorithms is thus proposed to optimise the performance of a video codec. The framework is designed to jointly minimize the CPU Time, Bit-rate and to maximize the quality of the compressed video stream. The thesis presents the use of this framework in the performance modelling and multi-objective optimisation of the most widely used video coding standard in practice at present, H.264 and the latest video coding standard, H.265/HEVC. When a communication network is used to transmit video, performance related parameters of the communication channel will impact the end-to-end performance of the video CODEC. Network delays and packet loss will impact the quality of the video that is received at the decoder via the communication channel, i.e., even if a video CODEC is optimally configured network conditions will make the experience sub-optimal. Given the above the thesis proposes a design, integration and testing of a novel approach to simulating a wired network and the use of UDP protocol for the transmission of video data. This network is subsequently used to simulate the impact of packet loss and network delays on optimally coded video based on the framework previously proposed for the modelling and optimisation of video CODECs. The quality of received video under different levels of packet loss and network delay is simulated, concluding the impact on transmitted video based on their content and features

Efficient HEVC-based video adaptation using transcoding

In a video transmission system, it is important to take into account the great diversity of the network/end-user constraints. On the one hand, video content is typically streamed over a network that is characterized by different bandwidth capacities. In many cases, the bandwidth is insufficient to transfer the video at its original quality. On the other hand, a single video is often played by multiple devices like PCs, laptops, and cell phones. Obviously, a single video would not satisfy their different constraints. These diversities of the network and devices capacity lead to the need for video adaptation techniques, e.g., a reduction of the bit rate or spatial resolution. Video transcoding, which modifies a property of the video without the change of the coding format, has been well-known as an efficient adaptation solution. However, this approach comes along with a high computational complexity, resulting in huge energy consumption in the network and possibly network latency. This presentation provides several optimization strategies for the transcoding process of HEVC (the latest High Efficiency Video Coding standard) video streams. First, the computational complexity of a bit rate transcoder (transrater) is reduced. We proposed several techniques to speed-up the encoder of a transrater, notably a machine-learning-based approach and a novel coding-mode evaluation strategy have been proposed. Moreover, the motion estimation process of the encoder has been optimized with the use of decision theory and the proposed fast search patterns. Second, the issues and challenges of a spatial transcoder have been solved by using machine-learning algorithms. Thanks to their great performance, the proposed techniques are expected to significantly help HEVC gain popularity in a wide range of modern multimedia applications

A reduced reference video quality assessment method for provision as a service over SDN/NFV-enabled networks

139 p.The proliferation of multimedia applications and services has generarted a noteworthy upsurge in network traffic regarding video content and has created the need for trustworthy service quality assessment methods. Currently, predominent position among the technological trends in telecommunication networkds are Network Function Virtualization (NFV), Software Defined Networking (SDN) and 5G mobile networks equipped with small cells. Additionally Video Quality Assessment (VQA) methods are a very useful tool for both content providers and network operators, to understand of how users perceive quality and this study the feasibility of potential services and adapt the network available resources to satisfy the user requirements

A reduced reference video quality assessment method for provision as a service over SDN/NFV-enabled networks

139 p.The proliferation of multimedia applications and services has generarted a noteworthy upsurge in network traffic regarding video content and has created the need for trustworthy service quality assessment methods. Currently, predominent position among the technological trends in telecommunication networkds are Network Function Virtualization (NFV), Software Defined Networking (SDN) and 5G mobile networks equipped with small cells. Additionally Video Quality Assessment (VQA) methods are a very useful tool for both content providers and network operators, to understand of how users perceive quality and this study the feasibility of potential services and adapt the network available resources to satisfy the user requirements

DEEP LEARNING FOR IMAGE RESTORATION AND ROBOTIC VISION

Traditional model-based approach requires the formulation of mathematical model, and the model often has limited performance. The quality of an image may degrade due to a variety of reasons: It could be the context of scene is affected by weather conditions such as haze, rain, and snow; It\u27s also possible that there is some noise generated during image processing/transmission (e.g., artifacts generated during compression.). The goal of image restoration is to restore the image back to desirable quality both subjectively and objectively. Agricultural robotics is gaining interest these days since most agricultural works are lengthy and repetitive. Computer vision is crucial to robots especially the autonomous ones. However, it is challenging to have a precise mathematical model to describe the aforementioned problems. Compared with traditional approach, learning-based approach has an edge since it does not require any model to describe the problem. Moreover, learning-based approach now has the best-in-class performance on most of the vision problems such as image dehazing, super-resolution, and image recognition. In this dissertation, we address the problem of image restoration and robotic vision with deep learning. These two problems are highly related with each other from a unique network architecture perspective: It is essential to select appropriate networks when dealing with different problems. Specifically, we solve the problems of single image dehazing, High Efficiency Video Coding (HEVC) loop filtering and super-resolution, and computer vision for an autonomous robot. Our technical contributions are threefold: First, we propose to reformulate haze as a signal-dependent noise which allows us to uncover it by learning a structural residual. Based on our novel reformulation, we solve dehazing with recursive deep residual network and generative adversarial network which emphasizes on objective and perceptual quality, respectively. Second, we replace traditional filters in HEVC with a Convolutional Neural Network (CNN) filter. We show that our CNN filter could achieve 7% BD-rate saving when compared with traditional filters such as bilateral and deblocking filter. We also propose to incorporate a multi-scale CNN super-resolution module into HEVC. Such post-processing module could improve visual quality under extremely low bandwidth. Third, a transfer learning technique is implemented to support vision and autonomous decision making of a precision pollination robot. Good experimental results are reported with real-world data