6,572 research outputs found
Recover Subjective Quality Scores from Noisy Measurements
Simple quality metrics such as PSNR are known to not correlate well with
subjective quality when tested across a wide spectrum of video content or
quality regime. Recently, efforts have been made in designing objective quality
metrics trained on subjective data (e.g. VMAF), demonstrating better
correlation with video quality perceived by human. Clearly, the accuracy of
such a metric heavily depends on the quality of the subjective data that it is
trained on. In this paper, we propose a new approach to recover subjective
quality scores from noisy raw measurements, using maximum likelihood
estimation, by jointly estimating the subjective quality of impaired videos,
the bias and consistency of test subjects, and the ambiguity of video contents
all together. We also derive closed-from expression for the confidence interval
of each estimate. Compared to previous methods which partially exploit the
subjective information, our approach is able to exploit the information in
full, yielding tighter confidence interval and better handling of outliers
without the need for z-scoring or subject rejection. It also handles missing
data more gracefully. Finally, as side information, it provides interesting
insights on the test subjects and video contents.Comment: 16 pages; abridged version appeared in Data Compression Conference
(DCC) 201
Simultaneous Measurement Imputation and Outcome Prediction for Achilles Tendon Rupture Rehabilitation
Achilles Tendon Rupture (ATR) is one of the typical soft tissue injuries.
Rehabilitation after such a musculoskeletal injury remains a prolonged process
with a very variable outcome. Accurately predicting rehabilitation outcome is
crucial for treatment decision support. However, it is challenging to train an
automatic method for predicting the ATR rehabilitation outcome from treatment
data, due to a massive amount of missing entries in the data recorded from ATR
patients, as well as complex nonlinear relations between measurements and
outcomes. In this work, we design an end-to-end probabilistic framework to
impute missing data entries and predict rehabilitation outcomes simultaneously.
We evaluate our model on a real-life ATR clinical cohort, comparing with
various baselines. The proposed method demonstrates its clear superiority over
traditional methods which typically perform imputation and prediction in two
separate stages
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