A comprehensive video codec comparison

In this paper, we compare the video codecs AV1 (version 1.0.0-2242 from August 2019), HEVC (HM and x265), AVC (x264), the exploration software JEM which is based on HEVC, and the VVC (successor of HEVC) test model VTM (version 4.0 from February 2019) under two fair and balanced configurations: All Intra for the assessment of intra coding and Maximum Coding Efficiency with all codecs being tuned for their best coding efficiency settings. VTM achieves the highest coding efficiency in both configurations, followed by JEM and AV1. The worst coding efficiency is achieved by x264 and x265, even in the placebo preset for highest coding efficiency. AV1 gained a lot in terms of coding efficiency compared to previous versions and now outperforms HM by 24% BD-Rate gains. VTM gains 5% over AV1 in terms of BD-Rates. By reporting separate numbers for JVET and AOM test sequences, it is ensured that no bias in the test sequences exists. When comparing only intra coding tools, it is observed that the complexity increases exponentially for linearly increasing coding efficiency

Efficient Coding of Transform Coefficient Levels in Hybrid Video Coding

All video coding standards of practical importance, such as Advanced Video Coding (AVC), its successor High Efficiency Video Coding (HEVC), and the state-of-the-art Versatile Video Coding (VVC), follow the basic principle of block-based hybrid video coding. In such an architecture, the video pictures are partitioned into blocks. Each block is first predicted by either intra-picture or motion-compensated prediction, and the resulting prediction errors, referred to as residuals, are compressed using transform coding. This thesis deals with the entropy coding of quantization indices for transform coefficients, also referred to as transform coefficient levels, as well as the entropy coding of directly quantized residual samples. The entropy coding of quantization indices is referred to as level coding in this thesis. The presented developments focus on both improving the coding efficiency and reducing the complexity of the level coding for HEVC and VVC. These goals were achieved by modifying the context modeling and the binarization of the level coding. The first development presented in this thesis is a transform coefficient level coding for variable transform block sizes, which was introduced in HEVC. It exploits the fact that non-zero levels are typically concentrated in certain parts of the transform block by partitioning blocks larger than \square{4} samples into \square{4} sub-blocks. Each \square{4} sub-block is then coded similarly to the level coding specified in AVC for \square{4} transform blocks. This sub-block processing improves coding efficiency and has the advantage that the number of required context models is independent of the set of supported transform block sizes. The maximum number of context-coded bins for a transform coefficient level is one indicator for the complexity of the entropy coding. An adaptive binarization of absolute transform coefficient levels using Rice codes is presented that reduces the maximum number of context-coded bins from 15 (as used in AVC) to three for HEVC. Based on the developed selection of an appropriate Rice code for each scanning position, this adaptive binarization achieves virtually the same coding efficiency as the binarization specified in AVC for bit-rate operation points typically used in consumer applications. The coding efficiency is improved for high bit-rate operation points, which are used in more advanced and professional applications. In order to further improve the coding efficiency for HEVC and VVC, the statistical dependencies among the transform coefficient levels of a transform block are exploited by a template-based context modeling developed in this thesis. Instead of selecting the context model for a current scanning position primarily based on its location inside a transform block, already coded neighboring locations inside a local template are utilized. To further increase the coding efficiency achieved by the template-based context modeling, the different coding phases of the initially developed level coding are merged into a single coding phase. As a consequence, the template-based context modeling can utilize the absolute levels of the neighboring frequency locations, which provides better conditional probability estimates and further improves coding efficiency. This template-based context modeling with a single coding phase is also suitable for trellis-coded quantization (TCQ), since TCQ is state-driven and derives the next state from the current state and the parity of the current level. TCQ introduces different context model sets for coding the significance flag depending on the current state. Based on statistical analyses, an extension of the state-dependent context modeling of TCQ is presented, which further improves the coding efficiency in VVC. After that, a method to reduce the complexity of the level coding at the decoder is presented. This method separates the level coding into a coding phase exclusively consisting of context-coded bins and another one consisting of bypass-coded bins only. For retaining the state-dependent context selection, which significantly contributes to the coding efficiency of TCQ, a dedicated parity flag is introduced and coded with context models in the first coding phase. An adaptive approach is then presented that further reduces the worst-case complexity, effectively lowering the maximum number of context-coded bins per transform coefficient to 1.75 without negatively affecting the coding efficiency. In the last development presented in this thesis, a dedicated level coding for transform skip blocks, which often occur in screen content applications, is introduced for VVC. This dedicated level coding better exploits the statistical properties of directly quantized residual samples for screen content. Various modifications to the level coding improve the coding efficiency for this type of content. Examples for these modifications are a binarization with additional context-coded flags and the coding of the sign information with adaptive context models

On Sparse Coding as an Alternate Transform in Video Coding

In video compression, specifically in the prediction process, a residual signal is calculated by subtracting the predicted from the original signal, which represents the error of this process. This residual signal is usually transformed by a discrete cosine transform (DCT) from the pixel, into the frequency domain. It is then quantized, which filters more or less high frequencies (depending on a quality parameter). The quantized signal is then entropy encoded usually by a context-adaptive binary arithmetic coding engine (CABAC), and written into a bitstream. In the decoding phase the process is reversed. DCT and quantization in combination are efficient tools, but they are not performing well at lower bitrates and creates distortion and side effect. The proposed method uses sparse coding as an alternate transform which compresses well at lower bitrates, but not well at high bitrates. The decision which transform is used is based on a rate-distortion optimization (RDO) cost calculation to get both transforms in their optimal performance range. The proposed method is implemented in high efficient video coding (HEVC) test model HM-16.18 and high efficient video coding for screen content coding (HEVC-SCC) for test model HM-16.18+SCM-8.7, with a Bjontegaard rate difference (BD-rate) saving, which archives up to 5.5%, compared to the standard

Random access prediction structures for light field video coding with MV-HEVC

Computational imaging and light field technology promise to deliver the required six-degrees-of-freedom for natural scenes in virtual reality. Already existing extensions of standardized video coding formats, such as multi-view coding and multi-view plus depth, are the most conventional light field video coding solutions at the moment. The latest multi-view coding format, which is a direct extension of the high efficiency video coding (HEVC) standard, is called multi-view HEVC (or MV-HEVC). MV-HEVC treats each light field view as a separate video sequence, and uses syntax elements similar to standard HEVC for exploiting redundancies between neighboring views. To achieve this, inter-view and temporal prediction schemes are deployed with the aim to find the most optimal trade-off between coding performance and reconstruction quality. The number of possible prediction structures is unlimited and many of them are proposed in the literature. Although some of them are efficient in terms of compression ratio, they complicate random access due to the dependencies on previously decoded pixels or frames. Random access is an important feature in video delivery, and a crucial requirement in multi-view video coding. In this work, we propose and compare different prediction structures for coding light field video using MV-HEVC with a focus on both compression efficiency and random accessibility. Experiments on three different short-baseline light field video sequences show the trade-off between bit-rate and distortion, as well as the average number of decoded views/frames, necessary for displaying any random frame at any time instance. The findings of this work indicate the most appropriate prediction structure depending on the available bandwidth and the required degree of random access

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

Dense light field coding: a survey

Light Field (LF) imaging is a promising solution for providing more immersive and closer to reality multimedia experiences to end-users with unprecedented creative freedom and flexibility for applications in different areas, such as virtual and augmented reality. Due to the recent technological advances in optics, sensor manufacturing and available transmission bandwidth, as well as the investment of many tech giants in this area, it is expected that soon many LF transmission systems will be available to both consumers and professionals. Recognizing this, novel standardization initiatives have recently emerged in both the Joint Photographic Experts Group (JPEG) and the Moving Picture Experts Group (MPEG), triggering the discussion on the deployment of LF coding solutions to efficiently handle the massive amount of data involved in such systems. Since then, the topic of LF content coding has become a booming research area, attracting the attention of many researchers worldwide. In this context, this paper provides a comprehensive survey of the most relevant LF coding solutions proposed in the literature, focusing on angularly dense LFs. Special attention is placed on a thorough description of the different LF coding methods and on the main concepts related to this relevant area. Moreover, comprehensive insights are presented into open research challenges and future research directions for LF coding.info:eu-repo/semantics/publishedVersio