The deep learning revolution has strongly impacted low-level image processing tasks such as style/domain transfer, enhancement/restoration, and visual quality assessments. Despite often being treated separately, the aforementioned tasks share a common theme of understanding, editing, or enhancing the appearance of input images without modifying the underlying content. We leverage this observation to develop a novel disentangled representation learning method that decomposes inputs into content and appearance features. The model is trained in a self-supervised manner and we use the learned features to develop a new quality prediction model named DisQUE. We demonstrate through extensive evaluations that DisQUE achieves state-of-the-art accuracy across quality prediction tasks and distortion types. Moreover, we demonstrate that the same features may also be used for image processing tasks such as HDR tone mapping, where the desired output characteristics may be tuned using example input-output pairs.

The share of online video traffic in global carbon dioxide emissions is growing steadily. To comply with the demand for video media, dedicated compression techniques are continuously optimized, but at the expense of increasingly higher computational demands and thus rising energy consumption at the video encoder side. In order to find the best trade-off between compression and energy consumption, modeling encoding energy for a wide range of encoding parameters is crucial. We propose an encoding time and energy model for SVT-AV1 based on empirical relations between the encoding time and video parameters as well as encoder configurations. Furthermore, we model the influence of video content by established content descriptors such as spatial and temporal information. We then use the predicted encoding time to estimate the required energy demand and achieve a prediction error of 19.6% for encoding time and 20.9% for encoding energy.

User satisfaction is significantly influenced by their expectations of video quality. Even when users are presented with identical video stimuli, the Quality of Experience (QoE) can vary based on the context. The acceptability and annoyance paradigm serves as a tool to understand this relationship by measuring QoE as a function of user expectations and video quality. Traditionally, subjective experiments assessing QoE have been conducted in controlled laboratory settings. While the extension of traditional video quality experiments to crowdsourcing settings is well-explored, the impact of crowdsourcing on QoE studies has not been thoroughly examined. This study explore the potential use of crowdsourcing platforms for acceptability & annoyance experiments. To this end, video quality and acceptability & annoyance experiments were conducted in both laboratory and crowdsourcing settings. The findings reveal a more linear relationship between video quality and QoE in crowdsourcing settings. Subjects in crowdsourcing settings tend to have higher expectations of video quality, resulting in a slight increase in acceptability & annoyance thresholds compared to laboratory experiments. Analyses suggest that extending acceptability & annoyance experiments to crowdsourcing is not as straightforward as extending traditional video quality experiments. In crowdsourcing settings, priming subject expectations with instructions is not as effective as it is in laboratory conditions.

Recently proposed perceptually optimized per-title video encoding methods provide better BD-rate savings than fixed bitrate-ladder approaches that have been employed in the past. However, a disadvantage of per-title encoding is that it requires significant time and energy to compute bitrate ladders. Over the past few years, a variety of methods have been proposed to construct optimal bitrate ladders including using low-level features to predict cross-over bitrates, optimal resolutions for each bitrate, predicting visual quality, etc. Here, we deploy features drawn from Visual Information Fidelity (VIF) (VIF features) extracted from uncompressed videos to predict the visual quality (VMAF) of compressed videos. We present multiple VIF feature sets extracted from different scales and subbands of a video to tackle the problem of bitrate ladder construction. Comparisons are made against a fixed bitrate ladder and a bitrate ladder obtained from exhaustive encoding using Bjontegaard delta metrics.

This work uses naive observers to compare two subjective studies conducted in a controlled laboratory environment on SDR HD, UHD, and HDR UHD contents. These tests aim to compare the precision and accuracy of a modified Degradation Category Rating (DCR) and Absolute Category Rating with Hidden Reference (ACR-HR) subjective methods for video quality assessment. The modified version of the DCR method includes a repetition of both reference and distorted stimuli; and utilizes an 11-grade rating scale from Expert Viewing Protocol (EVP) of ITU-R BT.500-15 standards. In the second subjective protocol, ACR-HR operates without repetition and with the 5-grade quality scale from ITU standards. We extensively analyze the scale usage and compare Mean Opinion Score (MOS) discriminability in both subjective studies. We show that both methods can retrieve accurate MOS. However, the ACR-HR method achieves better discriminability among MOS than DCR with the EVP rating scale while reducing the experimental effort by a factor of two, i.e., the cost of the experiment. The findings of this work give new insight into how to perform cost-efficient subjective tests for video quality estimation with naive observers and how to retrieve good MOS estimates.

Current developments in video encoding technology lead to continuously improving compression performance but at the expense of increasingly higher computational demands. Regarding the online video traffic increases during the last years and the concomitant need for video encoding, encoder complexity control mechanisms are required to restrict the processing time to a sufficient extent in order to find a reasonable trade-off between performance and complexity. We present a complexity control mechanism in SVT-AV1 by using speed-adaptive preset switching to comply with the remaining time budget. This method enables encoding with a user-defined time constraint within the complete preset range with an average precision of 8.9 % without introducing any additional latencies.