6 research outputs found
Perceptually-Driven Video Coding with the Daala Video Codec
The Daala project is a royalty-free video codec that attempts to compete with
the best patent-encumbered codecs. Part of our strategy is to replace core
tools of traditional video codecs with alternative approaches, many of them
designed to take perceptual aspects into account, rather than optimizing for
simple metrics like PSNR. This paper documents some of our experiences with
these tools, which ones worked and which did not. We evaluate which tools are
easy to integrate into a more traditional codec design, and show results in the
context of the codec being developed by the Alliance for Open Media.Comment: 19 pages, Proceedings of SPIE Workshop on Applications of Digital
Image Processing (ADIP), 201
The AV1 Constrained Directional Enhancement Filter (CDEF)
This paper presents the constrained directional enhancement filter designed
for the AV1 royalty-free video codec. The in-loop filter is based on a
non-linear low-pass filter and is designed for vectorization efficiency. It
takes into account the direction of edges and patterns being filtered. The
filter works by identifying the direction of each block and then adaptively
filtering with a high degree of control over the filter strength along the
direction and across it. The proposed enhancement filter is shown to improve
the quality of the Alliance for Open Media (AOM) AV1 and Thor video codecs in
particular in low complexity configurations.Comment: 5 page
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Perceptual video quality and quality of experience for adaptive video streaming
We live in a world where images and videos dominate our everyday lives. Every day, an enormous amount of video data is being shared in social media and consumer applications, while video streaming is becoming a new form of digital entertainment. Large-scale video streaming on demand has become possible thanks to numerous engineering achievements in fields such as video compression, high-speed computation and display technologies. Nevertheless, the skyrocketing needs for bandwidth and network resources consumed by video applications challenges modern video content delivery. Since the available bandwidth resources are limited, streaming service providers have to mediate between operation costs, bandwidth efficiency and maximizing user quality of experience. However, these goals are inherently conflicting and require knowledge of how user quality of experience is affected by the network-induced changes in video quality. Being able to understand and predict user quality of experience and perceptually optimize rate allocation, can have significant effects in better network utilization, reduced costs for service providers and improved user satisfaction. The goal of this dissertation is to study and predict user quality of experience in video streaming applications, by exploiting perceptual video quality and human behavioral responses to streaming-related video impairments. To this end, I present the details of three large-scale video subjective studies which target video streaming under multiple viewing conditions, such as display device, session duration, content characteristics and network/buffer conditions. By analyzing how humans react to changes in visual quality and streaming video impairments, I also design numerous video quality and quality of experience prediction models that can be used to evaluate the overall and the continuous-time perceived video quality. Throughout this dissertation, my goal is to perceptually optimize various stages of the video streaming pipeline, such as video encoding and video quality control as well as client-based rate adaptation. Ultimately, I envision that the outcome of this dissertation can be useful for video streaming applications at global scaleElectrical and Computer Engineerin