Lightning-Fast Dual-Layer Lossless Coding for Radiance Format High Dynamic Range Images

This paper proposes a fast dual-layer lossless coding for high dynamic range images (HDRIs) in the Radiance format. The coding, which consists of a base layer and a lossless enhancement layer, provides a standard dynamic range image (SDRI) without requiring an additional algorithm at the decoder and can losslessly decode the HDRI by adding the residual signals (residuals) between the HDRI and SDRI to the SDRI, if desired. To suppress the dynamic range of the residuals in the enhancement layer, the coding directly uses the mantissa and exponent information from the Radiance format. To further reduce the residual energy, each mantissa is modeled (estimated) as a linear function, i.e., a simple linear regression, of the encoded-decoded SDRI in each region with the same exponent. This is called simple linear regressive mantissa estimator. Experimental results show that, compared with existing methods, our coding reduces the average bitrate by approximately $1.57$-$6.68$ % and significantly reduces the average encoder implementation time by approximately $87.13$-$98.96$ %

Video QoS/QoE over IEEE802.11n/ac: A Contemporary Survey

The demand for video applications over wireless networks has tremendously increased, and IEEE 802.11 standards have provided higher support for video transmission. However, providing Quality of Service (QoS) and Quality of Experience (QoE) for video over WLAN is still a challenge due to the error sensitivity of compressed video and dynamic channels. This thesis presents a contemporary survey study on video QoS/QoE over WLAN issues and solutions. The objective of the study is to provide an overview of the issues by conducting a background study on the video codecs and their features and characteristics, followed by studying QoS and QoE support in IEEE 802.11 standards. Since IEEE 802.11n is the current standard that is mostly deployed worldwide and IEEE 802.11ac is the upcoming standard, this survey study aims to investigate the most recent video QoS/QoE solutions based on these two standards. The solutions are divided into two broad categories, academic solutions, and vendor solutions. Academic solutions are mostly based on three main layers, namely Application, Media Access Control (MAC) and Physical (PHY) which are further divided into two major categories, single-layer solutions, and cross-layer solutions. Single-layer solutions are those which focus on a single layer to enhance the video transmission performance over WLAN. Cross-layer solutions involve two or more layers to provide a single QoS solution for video over WLAN. This thesis has also presented and technically analyzed QoS solutions by three popular vendors. This thesis concludes that single-layer solutions are not directly related to video QoS/QoE, and cross-layer solutions are performing better than single-layer solutions, but they are much more complicated and not easy to be implemented. Most vendors rely on their network infrastructure to provide QoS for multimedia applications. They have their techniques and mechanisms, but the concept of providing QoS/QoE for video is almost the same because they are using the same standards and rely on Wi-Fi Multimedia (WMM) to provide QoS

Algorithms for compression of high dynamic range images and video

The recent advances in sensor and display technologies have brought upon the High Dynamic Range (HDR) imaging capability. The modern multiple exposure HDR sensors can achieve the dynamic range of 100-120 dB and LED and OLED display devices have contrast ratios of 10^5:1 to 10^6:1. Despite the above advances in technology the image/video compression algorithms and associated hardware are yet based on Standard Dynamic Range (SDR) technology, i.e. they operate within an effective dynamic range of up to 70 dB for 8 bit gamma corrected images. Further the existing infrastructure for content distribution is also designed for SDR, which creates interoperability problems with true HDR capture and display equipment. The current solutions for the above problem include tone mapping the HDR content to fit SDR. However this approach leads to image quality associated problems, when strong dynamic range compression is applied. Even though some HDR-only solutions have been proposed in literature, they are not interoperable with current SDR infrastructure and are thus typically used in closed systems. Given the above observations a research gap was identified in the need for efficient algorithms for the compression of still images and video, which are capable of storing full dynamic range and colour gamut of HDR images and at the same time backward compatible with existing SDR infrastructure. To improve the usability of SDR content it is vital that any such algorithms should accommodate different tone mapping operators, including those that are spatially non-uniform. In the course of the research presented in this thesis a novel two layer CODEC architecture is introduced for both HDR image and video coding. Further a universal and computationally efficient approximation of the tone mapping operator is developed and presented. It is shown that the use of perceptually uniform colourspaces for internal representation of pixel data enables improved compression efficiency of the algorithms. Further proposed novel approaches to the compression of metadata for the tone mapping operator is shown to improve compression performance for low bitrate video content. Multiple compression algorithms are designed, implemented and compared and quality-complexity trade-offs are identified. Finally practical aspects of implementing the developed algorithms are explored by automating the design space exploration flow and integrating the high level systems design framework with domain specific tools for synthesis and simulation of multiprocessor systems. The directions for further work are also presented

Análise do HEVC escalável : desempenho e controlo de débito

Mestrado em Engenharia Eletrónica e TelecomunicaçõesEsta dissertação apresenta um estudo da norma de codificação de vídeo de alta eficiência (HEVC) e a sua extensão para vídeo escalável, SHVC. A norma de vídeo SHVC proporciona um melhor desempenho quando codifica várias camadas em simultâneo do que quando se usa o codificador HEVC numa configuração simulcast. Ambos os codificadores de referência, tanto para a camada base como para a camada superior usam o mesmo modelo de controlo de débito, modelo R-λ, que foi otimizado para o HEVC. Nenhuma otimização de alocação de débito entre camadas foi até ao momento proposto para o modelo de testes (SHM 8) para a escalabilidade do HEVC (SHVC). Derivamos um novo modelo R-λ apropriado para a camada superior e para o caso de escalabilidade espacial, que conduziu a um ganho de BD-débito de 1,81% e de BD-PSNR de 0,025 em relação ao modelo de débito-distorção existente no SHM do SHVC. Todavia, mostrou-se também nesta dissertação que o proposto modelo de R-λ não deve ser usado na camada inferior (camada base) no SHVC e por conseguinte no HEVC.This dissertation provides a study of the High Efficiency Video Coding standard (HEVC) and its scalable extension, SHVC. The SHVC provides a better performance when encoding several layers simultaneously than using an HEVC encoder in a simulcast configuration. Both reference encoders, in the base layer and in the enhancement layer use the same rate control model, R-λ model, which was optimized for HEVC. No optimal bitrate partitioning amongst layers is proposed in scalable HEVC (SHVC) test model (SHM 8). We derived a new R-λ model for the enhancement layer and for the spatial case which led to a DB-rate gain of 1.81% and DB-PSNR gain of 0.025 in relation to the rate-distortion model of SHM-SHVC. Nevertheless, we also show in this dissertation that the proposed model of R-λ should not be used neither in the base layer nor in HEVC

Algorithms and methods for video transcoding.

Video transcoding is the process of dynamic video adaptation. Dynamic video adaptation can be defined as the process of converting video from one format to another, changing the bit rate, frame rate or resolution of the encoded video, which is mainly necessitated by the end user requirements. H.264 has been the predominantly used video compression standard for the last 15 years. HEVC (High Efficiency Video Coding) is the latest video compression standard finalised in 2013, which is an improvement over H.264 video compression standard. HEVC performs significantly better than H.264 in terms of the Rate-Distortion performance. As H.264 has been widely used in the last decade, a large amount of video content exists in H.264 format. There is a need to convert H.264 video content to HEVC format to achieve better Rate-Distortion performance and to support legacy video formats on newer devices. However, the computational complexity of HEVC encoder is 2-10 times higher than that of H.264 encoder. This makes it necessary to develop low complexity video transcoding algorithms to transcode from H.264 to HEVC format. This research work proposes low complexity algorithms for H.264 to HEVC video transcoding. The proposed algorithms reduce the computational complexity of H.264 to HEVC video transcoding significantly, with negligible loss in Rate-Distortion performance. This work proposes three different video transcoding algorithms. The MV-based mode merge algorithm uses the block mode and MV variances to estimate the split/non-split decision as part of the HEVC block prediction process. The conditional probability-based mode mapping algorithm models HEVC blocks of sizes 16×16 and lower as a function of H.264 block modes, H.264 and HEVC Quantisation Parameters (QP). The motion-compensated MB residual-based mode mapping algorithm makes the split/non-split decision based on content-adaptive classification models. With a combination of the proposed set of algorithms, the computational complexity of the HEVC encoder is reduced by around 60%, with negligible loss in Rate-Distortion performance, outperforming existing state-of-art algorithms by 20-25% in terms of computational complexity. The proposed algorithms can be used in computation-constrained video transcoding applications, to support video format conversion in smart devices, migration of large-scale H.264 video content from host servers to HEVC, cloud computing-based transcoding applications, and also to support high quality videos over bandwidth-constrained networks

Local Inverse Tone Curve Learning for High Dynamic Range Image Scalable Compression

International audienceThis paper presents a scalable high dynamic range (HDR) image coding scheme in which the base layer is a lowdynamic range (LDR) version of the image that may have been generated by an arbitrary Tone Mapping Operator (TMO). No restriction is imposed on the TMO, which can be either global or local, so as to fully respect the artistic intent of the producer. Our method successfully handles the case of complex local TMOs thanks to a block-wise and non-linear approach. A novel template based Inter Layer Prediction (ILP) is designed in order to perform the inverse tone mapping of a block without the need to transmit any additional parameter to the decoder. This method enables the use of a more accurate inverse tone mapping model than the simple linear regression commonly used for blockwise ILP. In addition, this paper shows that a linear adjustment of the initially predicted block can further improve the overall coding performance by using an efficient encoding scheme of the scaling parameters. Our experiments have shown an average bitrate saving of 47% on the HDR enhancement layer, compared to previous local ILP methods

Mobile app with steganography functionalities

[Abstract]: Steganography is the practice of hiding information within other data, such as images, audios, videos, etc. In this research, we consider applying this useful technique to create a mobile application that lets users conceal their own secret data inside other media formats, send that encoded data to other users, and even perform analysis to images that may have been under a steganography attack. For image steganography, lossless compression formats employ Least Significant Bit (LSB) encoding within Red Green Blue (RGB) pixel values. Reciprocally, lossy compression formats, such as JPEG, utilize data concealment in the frequency domain by altering the quantized matrices of the files. Video steganography follows two similar methods. In lossless video formats that permit compression, the LSB approach is applied to the RGB pixel values of individual frames. Meanwhile, in lossy High Efficient Video Coding (HEVC) formats, a displaced bit modification technique is used with the YUV components.[Resumo]: A esteganografía é a práctica de ocultar determinada información dentro doutros datos, como imaxes, audio, vídeos, etc. Neste proxecto pretendemos aplicar esta técnica como visión para crear unha aplicación móbil que permita aos usuarios ocultar os seus propios datos secretos dentro doutros formatos multimedia, enviar eses datos cifrados a outros usuarios e mesmo realizar análises de imaxes que puidesen ter sido comprometidas por un ataque esteganográfico. Para a esteganografía de imaxes, os formatos con compresión sen perdas empregan a codificación Least Significant Bit (LSB) dentro dos valores Red Green Blue (RGB) dos seus píxeles. Por outra banda, os formatos de compresión con perdas, como JPEG, usan a ocultación de datos no dominio de frecuencia modificando as matrices cuantificadas dos ficheiros. A esteganografía de vídeo segue dous métodos similares. En formatos de vídeo sen perdas, o método LSB aplícase aos valores RGB de píxeles individuais de cadros. En cambio, nos formatos High Efficient Video Coding (HEVC) con compresión con perdas, úsase unha técnica de cambio de bits nos compoñentes YUV.Traballo fin de grao (UDC.FIC). Enxeñaría Informática. Curso 2022/202

Multiview Video Coding for Virtual Reality

Virtual reality (VR) is one of the emerging technologies in recent years. It brings a sense of real world experience in simulated environments, hence, it is being used in many applications for example in live sporting events, music recordings and in many other interactive multimedia applications. VR makes use of multimedia content, and videos are a major part of it. VR videos are captured from multiple directions to cover the entire 360 field-of-view. It usually employs, multiple cameras of wide field-of-view such as fisheye lenses and the camera arrangement can also vary from linear to spherical set-ups. Videos in VR system are also subjected to constraints such as, variations in network bandwidth, heterogeneous mobile devices with limited decoding capacity, adaptivity for view switching in the display. The uncompressed videos from multiview cameras are redundant and impractical for storage and transmission. The existing video coding standards compresses the multiview videos effi ciently. However, VR systems place certain limitations on the video and camera arrangements, such as, it assumes rectilinear properties for video, translational motion model for prediction and the camera set-up to be linearly arranged. The aim of the thesis is to propose coding schemes which are compliant to the current video coding standards of H.264/AVC and its successor H.265/HEVC, the current state-of-the-art and multiview/scalable extensions. This thesis presents methods that compress the multiview videos which are captured from eight cameras that are arranged spherically, pointing radially outwards. The cameras produce circular fi sheye videos of 195 degree field-of-view. The final goal is to present methods, which optimize the bitrate in both storage and transmission of videos for the VR system. The presented methods can be categorized into two groups: optimizing storage bitrate and optimizing streaming bitrate of multiview videos. In the storage bitrate category, six methods were experimented. The presented methods competed against simulcast coding of individual views. The coding schemes were experimented with two data sets of 8 views each. The method of scalable coding with inter-layer prediction in all frames outperformed simulcast coding with approximately 7.9%. In the case of optimizing streaming birates, five methods were experimented. The method of scalable plus multiview skip-coding outperformed the simulcast method of coding by 36% on average. Future work will focus on pre-processing the fi sheye videos to rectilinear videos, in-order to fit them to the current translational model of the video coding standards. Moreover, the methods will be tested in comprehensive applications and system requirements

Error control strategies in H.265|HEVC video transmission

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonWith the rapid development in video coding technologies in the last decade, high-resolution video delivery suffers from packet loss due to unreliable transmission channels (time-varying characteristics). The error Resilience approaches at channel coding level are less efficient to implement in real time video transmission as the encoded video samples are in variable code length. Therefore, error resilience in video coding standard plays a vital role to reduce the effect of error propagation and improve the perceived visual quality. The main work in this thesis is to develop an efficient error resilience mechanism for H.265|HEVC video coding standard to reduce the effects of error propagation in error-prone conditions. In this thesis, two error resilience algorithms are proposed. The first one is Adaptive Slice Encoding (ASE) error resilience algorithm. The concept of this algorithm is to extract and protect the most active slices in the coded bitstream based on the adaptive search window. This algorithm can be applied in low delay video transmission with and without using a feedback channel. It is also designed to be compatible with reference coding software manual (HM16) for H.265|HEVC coding standard. The second proposed algorithm is a joint encoder-decoder error resilience called Error resilience based on Supplemental Enhancement Information (ERSEI) algorithm. A feedback message status is used from the decoder to notify the encoder to start encoding clean random-access picture adaptively based on the decoded picture hash message status from the decoder. At the same time, the decoder will be notified to start the error concealment process whilst waiting to receive correct video data. A recovery point message from the decoder feedback channel is used to update the encoder with error messages. In this thesis, extensive experimental work, evaluation, and comparison with state-of-the-art related algorithms have been conducted to evaluate the proposed algorithms. Furthermore, the best trade-off between the coding efficiency of the proposed error resilience algorithms and error resilience performance has been considered at the design stage. The experimental work evaluation includes both encoding conditions, i.e. error-free and error-prone. The results achieved from the experiments show significant improvements, in (Y-PSNR) results and subjective quality of the decoded bitstream, using the proposed algorithm in error-prone conditions with a variety of packet loss rates. Moreover, experimental work is conducted to test the algorithms complexity in terms of required processing execution time at both encoding and decoding stages. Additionally, the video coding standard performance for both H.264|AVC and H.265|HEVC coding standards are evaluated in error-free and error-prone environments. For ASE algorithm and when compared with improved region of interest (IROI) and region of interest (ROI) algorithms, a significant improvement in visual quality was the most obvious finding from the obtained results with PLRs of 2-18 (%). For ERSEI algorithm and when compared with the default HM16 with pixel copy concealment and motion compensated error concealment (MCEC) techniques, the evaluation results indicate clear visual quality enhancement under different packet loss rates PLRs (1,2 6, 8) %.The Ministry of Higher Education and Scientific Research in Ira