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

Exploring the design space of HEVC inverse transforms with dataflow programming

This paper presents the design space exploration of the hardware-based inverse fixed-point integer transform for High Efficiency Video Coding (HEVC). The designs are specified at high-level using CAL dataflow language and automatically synthesized to HDL for FPGA implementation. Several parallel design alternatives are proposed with trade-off between performance and resource. The HEVC transform consists of several independent components from 4x4 to 32x32 discrete cosine transform and 4x4 discrete sine transform.This work explores the strategies to efficiently compute the transforms by applying data parallelism on the different components. Results show that an intermediate version of parallelism, whereby the 4x4 and 8x8 are merged together, and the 16x16 and 32x32 merged together gives the best trade-off between performance and resource. The results presented in this work also give an insight on how the HEVC transform can be designed efficiently in parallel for hardware implementation

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

Scalable Light Field Compression Scheme Using Sparse Reconstruction and Restoration

International audienceThis paper describes a light field scalable compression scheme based on the sparsity of the angular Fourier transform of the light field. A subset of sub-aperture images (or views) is compressed using HEVC as a base layer and transmitted to the decoder. An entire light field is reconstructed from this view subset using a method exploiting the sparsity of the light field in the continuous Fourier domain. The reconstructed light field is enhanced using a patch-based restoration method. Then, restored samples are used to predict original ones, in a SHVC-based SNR-scalable scheme. Experiments with different datasets show a significant bit rate reduction of up to 24% in favor of the proposed compression method compared with a direct encoding of all the views with HEVC. The impact of the compression on the quality of the all-in-focus images is also analyzed showing the advantage of the proposed scheme

Adaptive Semantic Communications: Overfitting the Source and Channel for Profit

Most semantic communication systems leverage deep learning models to provide end-to-end transmission performance surpassing the established source and channel coding approaches. While, so far, research has mainly focused on architecture and model improvements, but such a model trained over a full dataset and ergodic channel responses is unlikely to be optimal for every test instance. Due to limitations on the model capacity and imperfect optimization and generalization, such learned models will be suboptimal especially when the testing data distribution or channel response is different from that in the training phase, as is likely to be the case in practice. To tackle this, in this paper, we propose a novel semantic communication paradigm by leveraging the deep learning model's overfitting property. Our model can for instance be updated after deployment, which can further lead to substantial gains in terms of the transmission rate-distortion (RD) performance. This new system is named adaptive semantic communication (ASC). In our ASC system, the ingredients of wireless transmitted stream include both the semantic representations of source data and the adapted decoder model parameters. Specifically, we take the overfitting concept to the extreme, proposing a series of ingenious methods to adapt the semantic codec or representations to an individual data or channel state instance. The whole ASC system design is formulated as an optimization problem whose goal is to minimize the loss function that is a tripartite tradeoff among the data rate, model rate, and distortion terms. The experiments (including user study) verify the effectiveness and efficiency of our ASC system. Notably, the substantial gain of our overfitted coding paradigm can catalyze semantic communication upgrading to a new era

Optimal Video Streaming in Dense 5G Networks With D2D Communications

© 2017 IEEE. Mobile video traffic and mobile devices have now outpaced other data traffic and fixed devices. Global service providers are attempting to propose new mobile infrastructures and solutions for high performance of video streaming services, i.e., high quality of experience (QoE) at high resource efficiency. Although device-to-device (D2D) communications have been an emerging technique that is anticipated to provide a massive number of mobile users with advanced services in 5G networks, the management of resource and co-channel interference between D2D pairs, i.e., helper-requester pairs, and cellular users (CUs) is challenging. In this paper, we design an optimal rate allocation and description distribution for high performance video streaming, particularly, achieving high QoE at high energy efficiency while limiting co-channel interference over D2D communications in 5G networks. To this end, we allocate optimal encoding rates to different layers of a video segment and then packetize the video segment into multiple descriptions with embedded forward error correction before transmission. Simultaneously, the optimal numbers of descriptions are distributed to D2D helpers and base stations in a cooperative scheme for transmitting to the D2D requesters. The optimal results are efficiently in correspondence with intra-popularity of different segments of a video characterized by requesters' behavior, characteristic of lossy wireless channels, channel state information of D2D requesters, and constraints on remaining energy of D2D helpers and target signal to interference plus noise ratio of CUs. Simulation results demonstrate the benefits of our proposed solution in terms of high performance video streaming

Katseenseurannan sovellukset mielenkiintoisen alueen HEVC-pakkaukselle

The increase in video streaming services and video resolutions has exploded the volume of Internet video traffic. New video coding standards, such as High Efficiency Video Coding (HEVC) have been developed to mitigate this inevitable video data explosion with better compression. The aim of video coding is to reduce the video size while maintaining the best possible perceived quality. Region of Interest (ROI) encoding particularly addresses this objective by focusing on the areas that humans would pay the most attention at and encode them with higher quality than the non-ROI areas. Methods for finding the ROI, and video encoding in general, take advantage of the Human Visual System (HVS). Computational HVS models can be used for the ROI detection but all current state-of-the-art models are designed for still images. Eye tracking data can be used for creating and verifying these models, including models suitable for video, which in turn calls for a reliable way to collect eye tracking data. Eye tracking glasses allow the widest range of possible scenarios out of all eye tracking equipment. Therefore, the glasses are used in this work to collect eye tracking data from 41 different videos. The main contribution of this work is to present a real-time system using eye tracking data to enhance the perceived quality of the video. The proposed system makes use of video recorded from the scene camera of the eye tracking glasses and Kvazaar open-source HEVC encoder for video compression. The system was shown to provide better subjective quality over the native rate control algorithm of Kvazaar. The obtained results were evaluated with Eye tracking Weighted PSNR (EWPSNR) that represents the HVS better than traditional PSNR. The system is shown to achieve up to 33% bit rate reduction for the same EWPSNR and on average 5-10% reduction depending on the parameter set. Additionally, the encoding time is improved by 8-20%

Quality Evaluations and algorithmic Improvement of the next Generation Video Coding - HEVC

The increased processing power and screen sizes of mobile devices has made it desirable to watch multimedia presentations on the go. On such devices the data network bandwidth is usually the limiting factor, which imposes a tradeoff between quality and resolution on the presented content. A new video compression system called High Efficiency Video Coding (HEVC) is currently under development. The vision of HEVC is to create a compression system that achieves the same quality at half the bit rate compared to the existing H.264/AVC standard [2].The goal of this thesis is to investigate how HEVC performs compared to H.264/AVC using mobile platforms and sport content as the scenario. The subjective test was conducted on an Apple iPad. It indicated that HEVC has a clear gain in compression compared to H.264/AVC. On average at a resolution of 640x368, HEVC achieved a good quality rating at approximately 550 kilobit per second while H.264/AVC did almost reach this quality at 1000 kilobit per second. However, it was shown that subjective quality gain varied over content.The objective measurements showed an overall reduction in bit rate of 32% forthe luma component. However, the reduction of bit rate was highly variable over content and resolution. A high correlation between the subjective and objective measurements was found, which indicates that it was almost a linear relationship between the reported subjective and objective results.In addition, a proposed deblocking filter was implemented. The filter applies a new filter function of the luma samples and performs line based filtering decision. On average the reduction in bit rate was reported to be 0.4%, with a maximum reduction of 0.8% for the luma component. The decoding time relative to the second version of the HEVC test model was reported to be 1.5% higher. This is most likely due to the line based filtering decision. The general impression of HEVC is that it has the ability to reach the stated vision, and perhaps even surpass, when finalized

High throughput image compression and decompression on GPUs

Diese Arbeit befasst sich mit der Entwicklung eines GPU-freundlichen, intra-only, Wavelet-basierten Videokompressionsverfahrens mit hohem Durchsatz, das für visuell verlustfreie Anwendungen optimiert ist. Ausgehend von der Beobachtung, dass der JPEG 2000 Entropie-Kodierer ein Flaschenhals ist, werden verschiedene algorithmische Änderungen vorgeschlagen und bewertet. Zunächst wird der JPEG 2000 Selective Arithmetic Coding Mode auf der GPU realisiert, wobei sich die Erhöhung des Durchsatzes hierdurch als begrenzt zeigt. Stattdessen werden zwei nicht standard-kompatible Änderungen vorgeschlagen, die (1) jede Bitebebene in nur einem einzelnen Pass verarbeiten (Single-Pass-Modus) und (2) einen echten Rohcodierungsmodus einführen, der sample-weise parallelisierbar ist und keine aufwendige Kontextmodellierung erfordert. Als nächstes wird ein alternativer Entropiekodierer aus der Literatur, der Bitplane Coder with Parallel Coefficient Processing (BPC-PaCo), evaluiert. Er gibt Signaladaptivität zu Gunsten von höherer Parallelität auf und daher wird hier untersucht und gezeigt, dass ein aus verschiedensten Testsequenzen gemitteltes statisches Wahrscheinlichkeitsmodell eine kompetitive Kompressionseffizienz erreicht. Es wird zudem eine Kombination von BPC-PaCo mit dem Single-Pass-Modus vorgeschlagen, der den Speedup gegenüber dem JPEG 2000 Entropiekodierer von 2,15x (BPC-PaCo mit zwei Pässen) auf 2,6x (BPC-PaCo mit Single-Pass-Modus) erhöht auf Kosten eines um 0,3 dB auf 1,0 dB erhöhten Spitzen-Signal-Rausch-Verhältnis (PSNR). Weiter wird ein paralleler Algorithmus zur Post-Compression Ratenkontrolle vorgestellt sowie eine parallele Codestream-Erstellung auf der GPU. Es wird weiterhin ein theoretisches Laufzeitmodell formuliert, das es durch Benchmarking von einer GPU ermöglicht die Laufzeit einer Routine auf einer anderen GPU vorherzusagen. Schließlich wird der erste JPEG XS GPU Decoder vorgestellt und evaluiert. JPEG XS wurde als Low Complexity Codec konzipiert und forderte erstmals explizit GPU-Freundlichkeit bereits im Call for Proposals. Ab Bitraten über 1 bpp ist der Decoder etwa 2x schneller im Vergleich zu JPEG 2000 und 1,5x schneller als der schnellste hier vorgestellte Entropiekodierer (BPC-PaCo mit Single-Pass-Modus). Mit einer GeForce GTX 1080 wird ein Decoder Durchsatz von rund 200 fps für eine UHD-4:4:4-Sequenz erreicht.This work investigates possibilities to create a high throughput, GPU-friendly, intra-only, Wavelet-based video compression algorithm optimized for visually lossless applications. Addressing the key observation that JPEG 2000’s entropy coder is a bottleneck and might be overly complex for a high bit rate scenario, various algorithmic alterations are proposed. First, JPEG 2000’s Selective Arithmetic Coding mode is realized on the GPU, but the gains in terms of an increased throughput are shown to be limited. Instead, two independent alterations not compliant to the standard are proposed, that (1) give up the concept of intra-bit plane truncation points and (2) introduce a true raw-coding mode that is fully parallelizable and does not require any context modeling. Next, an alternative block coder from the literature, the Bitplane Coder with Parallel Coefficient Processing (BPC-PaCo), is evaluated. Since it trades signal adaptiveness for increased parallelism, it is shown here how a stationary probability model averaged from a set of test sequences yields competitive compression efficiency. A combination of BPC-PaCo with the single-pass mode is proposed and shown to increase the speedup with respect to the original JPEG 2000 entropy coder from 2.15x (BPC-PaCo with two passes) to 2.6x (proposed BPC-PaCo with single-pass mode) at the marginal cost of increasing the PSNR penalty by 0.3 dB to at most 1 dB. Furthermore, a parallel algorithm is presented that determines the optimal code block bit stream truncation points (given an available bit rate budget) and builds the entire code stream on the GPU, reducing the amount of data that has to be transferred back into host memory to a minimum. A theoretical runtime model is formulated that allows, based on benchmarking results on one GPU, to predict the runtime of a kernel on another GPU. Lastly, the first ever JPEG XS GPU-decoder realization is presented. JPEG XS was designed to be a low complexity codec and for the first time explicitly demanded GPU-friendliness already in the call for proposals. Starting at bit rates above 1 bpp, the decoder is around 2x faster compared to the original JPEG 2000 and 1.5x faster compared to JPEG 2000 with the fastest evaluated entropy coder (BPC-PaCo with single-pass mode). With a GeForce GTX 1080, a decoding throughput of around 200 fps is achieved for a UHD 4:4:4 sequence