Video Quality-of-Experience: Presentation Quality vs. Playback Smoothness, and Their Interactions

Delivering video content with the best visual quality-of-experience (QoE) to end users is the central goal of modern video distribution services. User’s QoE is determined by two factors – the quality of the video streamed to the user device, and the quality of the playback when displaying the video on user’s device. The former is typically measured as the presentation quality of the video, meaning the perceptual quality of the video streams when it is played on the user device at its intended speed without interruption. Typical processes and factors that could affect the presentation quality include encoding/transcoding at the hosting servers or networks, and variations of the user device parameters (size, brightness, resolution, etc.). The latter may be interpreted as the playback smoothness, for which the mostly encountered quality issue is video freezing. Freezing may occur either at the beginning before the video starts or in the middle of the playback. It may be caused by insufficient network bandwidth, network delay and instability, unhealthy device buffer and power conditions, and unmatched decoding/display speed, etc.

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The Challenge of Video Quality-of-Experience Management Across Resolutions and Viewing Devices

In real-world video delivery systems, video engineers are often faced with major challenges while trying to monitor and control the quality of the videos being delivered. Over the years, people have converged to the view that the quality-of-delivery (e.g., bandwidth allocation and smoothness of delivery, sometimes interpreted as quality-of-service) are the only parts of the overall picture. What really matters ultimately is the perceptual quality-of-experience (QoE) of the users at the very end of the video delivery chain. QoE is highly personal and may be influenced by many factors, but there are certain apects that will for sure strongly affect QoE, and are predictable and manageable even before the video reaches the end users. One such aspect is the impact of the viewing device, as well as the resolution (numbers of pixels in each row and column) that is used to display the video on that device. For example, a strongly compressed video may appear to have fine quality when viewed on a smartphone, but may exhibit annoying artifacts on a large size TV.

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How to Pick the Right Tone-Mapping Operators for HDR Content?

In the past few years, we have seen some great advancement in high dynamic range (HDR) imaging technologies. A significant trend in the content producing industry now is to capture video content with HDR. Meanwhile, leading video content distributors have also been moving at a fast pace in facilitating HDR videos to be delivered to consumers’ home and personal devices. At the other end of the delivery chain, however, how to best visualize the HDR content becomes a problem. Professional HDR displays that could faithfully preserve the creative intent by the content producers are quite expensive right now. Even the consumer HDR TVs of sub-par performance at reproducing the visual effect at post-production studios are at a pretty high price range. The reality is that in the next 5 or more years, even if HDR content can be successfully delivered to consumer devices, a large proportion of them will not be visualized using the displays with proper HDR capabilities.

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Why SSIM is not enough?

In an earlier blog, we provided an explanation on why the structural similarity (SSIM) index becomes by far the most popular objective model to predict perceived image/video quality, both in academia and industry. The contribution of SSIM is certainly outstanding, but is SSIM enough in real-world applications as the ultimate image/video quality measure or the ultimate image/video fidelity measure (considering its computation needs a reference image/video)? Read More

Why SSIM got popular?

The structural similarity (SSIM) index is a method to automatically predict perceived quality of images and videos. It is getting extremely popular in the past few years. In academia, the original SSIM paper published in 2004 has received over 10,000 Google Scholar citations so far, perhaps more than any paper in the literature of video engineering. It also received an IEEE Signal Processing Society Best Paper Award, one of the most prestigious paper awards in signal processing. In industry, the SSIM algorithm received the Primetime Engineering Emmy Award, one of the most prestigious technology awards in TV industry. In the citation by Television Academy, it says Read More