Video Compression and Optimization Technologies - Review

The use of video streaming is constantly increasing. High-resolution video requires resources on both the sender and the receiver side. There are many compression techniques that can be utilized to compress the video and simultaneously maintain quality. The main goal of this paper is to provide an overview of video streaming and QoE. This paper describes the basic concepts and discusses existing methodologies to measure QoE. Subjective, objective, and video compression technologies are discussed. This review paper gathers the codec implementation developed by MPEG, Google, and Apple. This paper outlines the challenges and future research directions that should be considered in the measurement and assessment of quality of experience for video services

Methodology for Modeling and Comparing Video Codecs: HEVC, EVC, and VVC

Online videos are the major source of internet traffic, and are about to become the largest majority. Increasing effort is aimed to developing more efficient video codecs. In order to compare existing and novel video codecs, this paper presents a simple but effective methodology to model their performance in terms of Rate Distortion (RD). A linear RD model in the dB variables, Peak Signal-to-Noise Ratio (PSNR) and Bitrate (BR), easily allows us to estimate the difference in PSNR or BR between two sets of encoding conditions. Six sequences from the MPEG test set with the same resolution, encoded at different BR and different Quantization Parameters, were used to create the data set to estimate each RD model. Three codecs (HEVC, EVC, and VVC) were compared with this methodology, after estimating their models. Fitting properties of each model and a performance comparison between the models are finally shown and discussed

Analisis Perbandingan Teknik Video Codec H.264/AVC, H.265/HEVC, VP9 dan AV1

Video applications consume more energy on the Internet and can be accessed by electronic devices, due to an increase in the consumption of high-resolution and high-quality video content, presenting serious issues to delivery infrastructure that needs higher video compression technologies. The focus of this paper is to evaluate the quality of the most current codec, AV1, to its predecessor codec. The comparison was made experimentally at two video resolutions (1080p and 720p) by sampling video frames with various CRF/CQP values and testing several parameters analyses such as encoding duration, compression ratio, bit rate, Mean Square Error (MSE), and Peak Signal to Noise Ratio (PSNR). The AV1 codec is very great in terms of quality and file size, even though it is slower in terms of compression speed. The H.265/HEVC codec, on the other side, beats the other codec in terms of compression ratio. In conclusion, the H.265/HEVC codec is suggested as a material for obtaining a well compressed video with small file size and a short time.Video applications consume more energy on the Internet and can be accessed by electronic devices, due to an increase in the consumption of high-resolution and high-quality video content, presenting serious issues to delivery infrastructure that needs higher video compression technologies. The focus of this paper is to evaluate the quality of the most current codec, AV1, to its predecessor codec. The comparison was made experimentally at two video resolutions (1080p and 720p) by sampling video frames with various CRF/CQP values and testing several parameters analyses such as encoding duration, compression ratio, bit rate, Mean Square Error (MSE), and Peak Signal to Noise Ratio (PSNR). The AV1 codec is very great in terms of quality and file size, even though it is slower in terms of compression speed. The H.265/HEVC codec, on the other side, beats the other codec in terms of compression ratio. In conclusion, the H.265/HEVC codec is suggested as a material for obtaining a well compressed video with small file size and a short time

Methodology for modeling and comparing video codecs: Hevc, evc, and vvc

none3noOnline videos are the major source of internet traffic, and are about to become the largest majority. Increasing effort is aimed to developing more efficient video codecs. In order to compare existing and novel video codecs, this paper presents a simple but effective methodology to model their performance in terms of Rate Distortion (RD). A linear RD model in the dB variables, Peak Signal-to-Noise Ratio (PSNR) and Bitrate (BR), easily allows us to estimate the difference in PSNR or BR between two sets of encoding conditions. Six sequences from the MPEG test set with the same resolution, encoded at different BR and different Quantization Parameters, were used to create the data set to estimate each RD model. Three codecs (HEVC, EVC, and VVC) were compared with this methodology, after estimating their models. Fitting properties of each model and a performance comparison between the models are finally shown and discussed.openBattista S.; Conti M.; Orcioni S.Battista, S.; Conti, M.; Orcioni, S

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

User generated HDR gaming video streaming : dataset, codec comparison and challenges

Gaming video streaming services have grown tremendously in the past few years, with higher resolutions, higher frame rates and HDR gaming videos getting increasingly adopted among the gaming community. Since gaming content as such is different from non-gaming content, it is imperative to evaluate the performance of the existing encoders to help understand the bandwidth requirements of such services, as well as further improve the compression efficiency of such encoders. Towards this end, we present in this paper GamingHDRVideoSET, a dataset consisting of eighteen 10-bit UHD-HDR gaming videos and encoded video sequences using four different codecs, together with their objective evaluation results. The dataset is available online at [to be added after paper acceptance]. Additionally, the paper discusses the codec compression efficiency of most widely used practical encoders, i.e., x264 (H.264/AVC), x265 (H.265/HEVC) and libvpx (VP9), as well the recently proposed encoder libaom (AV1), on 10-bit, UHD-HDR content gaming content. Our results show that the latest compression standard AV1 results in the best compression efficiency, followed by HEVC, H.264, and VP9.Comment: 14 pages, 8 figures, submitted to IEEE journa

Performance comparison of video encoders in light field image compression

Efficient compression plays a significant role in Light Fieldimaging technology because of the huge amount of data neededfor their representation. Video encoders using different strategiesare commonly used for Light Field image compression. In this pa-per, different video encoder implementations including HM, VTM,x265, xvc, VP9, and AV1 are analysed and compared in termsof coding efficiency, and encoder/decoder time-complexity. Lightfield images are compressed as pseudo-videos

A Survey on Energy Consumption and Environmental Impact of Video Streaming

Climate change challenges require a notable decrease in worldwide greenhouse gas (GHG) emissions across technology sectors. Digital technologies, especially video streaming, accounting for most Internet traffic, make no exception. Video streaming demand increases with remote working, multimedia communication services (e.g., WhatsApp, Skype), video streaming content (e.g., YouTube, Netflix), video resolution (4K/8K, 50 fps/60 fps), and multi-view video, making energy consumption and environmental footprint critical. This survey contributes to a better understanding of sustainable and efficient video streaming technologies by providing insights into the state-of-the-art and potential future directions for researchers, developers, and engineers, service providers, hosting platforms, and consumers. We widen this survey's focus on content provisioning and content consumption based on the observation that continuously active network equipment underneath video streaming consumes substantial energy independent of the transmitted data type. We propose a taxonomy of factors that affect the energy consumption in video streaming, such as encoding schemes, resource requirements, storage, content retrieval, decoding, and display. We identify notable weaknesses in video streaming that require further research for improved energy efficiency: (1) fixed bitrate ladders in HTTP live streaming; (2) inefficient hardware utilization of existing video players; (3) lack of comprehensive open energy measurement dataset covering various device types and coding parameters for reproducible research