4,281 research outputs found
Saliency-Aware Spatio-Temporal Artifact Detection for Compressed Video Quality Assessment
Compressed videos often exhibit visually annoying artifacts, known as
Perceivable Encoding Artifacts (PEAs), which dramatically degrade video visual
quality. Subjective and objective measures capable of identifying and
quantifying various types of PEAs are critical in improving visual quality. In
this paper, we investigate the influence of four spatial PEAs (i.e. blurring,
blocking, bleeding, and ringing) and two temporal PEAs (i.e. flickering and
floating) on video quality. For spatial artifacts, we propose a visual saliency
model with a low computational cost and higher consistency with human visual
perception. In terms of temporal artifacts, self-attention based TimeSFormer is
improved to detect temporal artifacts. Based on the six types of PEAs, a
quality metric called Saliency-Aware Spatio-Temporal Artifacts Measurement
(SSTAM) is proposed. Experimental results demonstrate that the proposed method
outperforms state-of-the-art metrics. We believe that SSTAM will be beneficial
for optimizing video coding techniques
Cross-Recurrence Quantification Analysis of Categorical and Continuous Time Series: an R package
This paper describes the R package crqa to perform cross-recurrence
quantification analysis of two time series of either a categorical or
continuous nature. Streams of behavioral information, from eye movements to
linguistic elements, unfold over time. When two people interact, such as in
conversation, they often adapt to each other, leading these behavioral levels
to exhibit recurrent states. In dialogue, for example, interlocutors adapt to
each other by exchanging interactive cues: smiles, nods, gestures, choice of
words, and so on. In order for us to capture closely the goings-on of dynamic
interaction, and uncover the extent of coupling between two individuals, we
need to quantify how much recurrence is taking place at these levels. Methods
available in crqa would allow researchers in cognitive science to pose such
questions as how much are two people recurrent at some level of analysis, what
is the characteristic lag time for one person to maximally match another, or
whether one person is leading another. First, we set the theoretical ground to
understand the difference between 'correlation' and 'co-visitation' when
comparing two time series, using an aggregative or cross-recurrence approach.
Then, we describe more formally the principles of cross-recurrence, and show
with the current package how to carry out analyses applying them. We end the
paper by comparing computational efficiency, and results' consistency, of crqa
R package, with the benchmark MATLAB toolbox crptoolbox. We show perfect
comparability between the two libraries on both levels
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