Modern video encoders are complex software containing dozens of parameters, which allows them to be configured to different scenarios, requirements, or specific titles or scenes. Besides the number of parameters, the ...
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ISBN:
(纸本)9798350387261;9798350387254
Modern video encoders are complex software containing dozens of parameters, which allows them to be configured to different scenarios, requirements, or specific titles or scenes. Besides the number of parameters, the inter-dependency between them adds to the complexity of finding a per-title optimized combination of encoding parameters. Even though good practices in the industry have emerged, with the definition of presets per content type (e.g., film vs. cartoon), such practices are suboptimal for specific titles or scenes. Indeed, finding the best encoding parameters for a piece of content is currently a mix of best practices and trial-and-error artwork. We propose an efficient video encoder autotuner based on offline Bayesian optimization and supervised machine learning. Our proposal uses Bayesian optimization to search for a per-title best encoding parameter set offline to generate a dataset. Then, we use the generated dataset to train machine learning models that can map features extracted from the content to the best encoding parameters. Our experiments show that our generated dataset can find a combination of parameters that improves up to approximately -14.49% BD-Rate (0.77 BD-PSNR) and -11.59% BD-Rate (2.12 BD-VMAF) when optimizing for PSNR and VMAF, respectively. In comparison, our prediction models can recover similar to 80% of such performance while requiring only one fast encoding (compared to hundreds of encodes of a search optimization).
HTTP adaptive streaming with chunked transfer encoding can offer low-latency streaming without sacrificing the coding efficiency. This allows media segments to be delivered while still being packaged. However, convent...
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HTTP adaptive streaming with chunked transfer encoding can offer low-latency streaming without sacrificing the coding efficiency. This allows media segments to be delivered while still being packaged. However, conventional schemes often make widely inaccurate bandwidth measurements due to the presence of idle periods between the chunks and hence this is causing sub-optimal adaptation decisions. To address this issue, we earlier proposed ACTE (ABR for Chunked Transfer encoding) [6], a bandwidth prediction scheme for low-latency chunked streaming. While ACTE was a significant step forward, in this study we focus on two still remaining open areas, namely, (i) quantifying the impact of encoding parameters, including chunk and segment durations, bitrate levels, minimum interval between IDR-frames and frame rate on ACTE, and (ii) exploring the impact of video content complexity on ACTE. We thoroughly investigate these questions and report on our findings. We also discuss some additional issues that arise in the context of pursuing very low latency HTTP video streaming.
With the unprecedented growth of mobile video data traffic every year, increasing users' video quality of experience (QoE) under limited network radio resources becomes a critical issue in next-generation cellular...
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With the unprecedented growth of mobile video data traffic every year, increasing users' video quality of experience (QoE) under limited network radio resources becomes a critical issue in next-generation cellular systems. However, videos compressed with unsuitable video encoding parameters will waste the network radio resources and users even may not be able to get satisfied video QoE. Specifically, a video with higher bit rate requirement (determined by the video encoding parameters) does not imply that a user will have better QoE. This paper studies the QoE optimization problem via carefully determining a combination of video encoding parameters for each user under the radio resource block constraint. The objective is to maximize the total QoE of all users. We prove that our target problem is NP-hard and propose an algorithm based on dynamic programming to solve the problem. Then, we prove that our proposed algorithm is a pseudo-polynomial time optimal algorithm. We construct a series of simulations with realistic video sequences encoded by H.264 and network settings to evaluate the performance of our proposed algorithm. Compared with two baselines, the simulation results show that the proposed algorithm can significantly improve the total QoE of all users and indicate that the video encoding adaptation is an important issue in the QoE optimization problem.
The emerging H.265 High Efficiency Video Coding (HEVC) standard for video compression is geared towards providing high quality / high resolution video at low to moderate bit rates; thus providing a significant increas...
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ISBN:
(纸本)9781629938370
The emerging H.265 High Efficiency Video Coding (HEVC) standard for video compression is geared towards providing high quality / high resolution video at low to moderate bit rates; thus providing a significant increase in compression efficiency over existing standards like H.262/263/264. As is typically the case, increased compression results in decreased coding redundancy. Hence from a video quality perspective, of interest would be the nature of potential coding artifacts as well as the profile of video failures or dropouts arising due to uncorrected coding errors. In the latter case, the occurrence of these uncorrectable errors would depend upon external factors like the channel SNR or Eb/No profile, coupled with the source coding efficiency (or redundancy); the latter being governed by the opposing constraints of picture quality versus allowable bit rate or channel bandwidth. — The initial portion of this work is focused on dropout profiles due to randomly occurring bitstream errors. The geometries of the manifested dropout regions are quite noticeable as compared to those observed in traditional slice based video syntax for the same content, under a similar random error profile. This uniqueness impacts the spatial dropout detection algorithms to be used. — In the latter part of this work, video quality for a variety of content applications (like sports, news/talk shows, animation, etc…) and under varying encoder parameter profiles is monitored. The goal is to optimize the HEVC encoding parameters such as Transform Unit size, Coding Unit size and Maximum Partition Depth for video quality, for each chosen content application.
MP3 audio is a promising carrier format for covert communication because of its popularization. In this paper, we propose an MP3 steganographic method by exploiting the rule of window switching during encoding. The me...
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MP3 audio is a promising carrier format for covert communication because of its popularization. In this paper, we propose an MP3 steganographic method by exploiting the rule of window switching during encoding. The method carries out embedding by establishing a mapping relationship between the secret bit and the encoding parameter, namely window type. The proposed algorithm is fully compliant with MP3 compression standard and the distortion caused by steganography can be controlled automatically by the distortion adjustment mechanism of the encoder. Experimental results demonstrate that the proposed method introduces insignificant perceptual distortion and is statistically undetectable for the attack of block size analysis. (c) 2012 Elsevier Ltd. All rights reserved.
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