16 research outputs found
Bit-depth enhancement detection for compressed video
In recent years, display intensity and contrast have increased considerably.
Many displays support high dynamic range (HDR) and 10-bit color depth. Since
high bit-depth is an emerging technology, video content is still largely shot
and transmitted with a bit depth of 8 bits or less per color component.
Insufficient bit-depths produce distortions called false contours or banding,
and they are visible on high contrast screens. To deal with such distortions,
researchers have proposed algorithms for bit-depth enhancement
(dequantization). Such techniques convert videos with low bit-depth (LBD) to
videos with high bit-depth (HBD). The quality of converted LBD video, however,
is usually lower than that of the original HBD video, and many consumers prefer
to keep the original HBD versions. In this paper, we propose an algorithm to
determine whether a video has undergone conversion before compression. This
problem is complex; it involves detecting outcomes of different dequantization
algorithms in the presence of compression that strongly affects the
least-significant bits (LSBs) in the video frames. Our algorithm can detect
bit-depth enhancement and demonstrates good generalization capability, as it is
able to determine whether a video has undergone processing by dequantization
algorithms absent from the training dataset
Video compression dataset and benchmark of learning-based video-quality metrics
Video-quality measurement is a critical task in video processing. Nowadays,
many implementations of new encoding standards - such as AV1, VVC, and LCEVC -
use deep-learning-based decoding algorithms with perceptual metrics that serve
as optimization objectives. But investigations of the performance of modern
video- and image-quality metrics commonly employ videos compressed using older
standards, such as AVC. In this paper, we present a new benchmark for
video-quality metrics that evaluates video compression. It is based on a new
dataset consisting of about 2,500 streams encoded using different standards,
including AVC, HEVC, AV1, VP9, and VVC. Subjective scores were collected using
crowdsourced pairwise comparisons. The list of evaluated metrics includes
recent ones based on machine learning and neural networks. The results
demonstrate that new no-reference metrics exhibit a high correlation with
subjective quality and approach the capability of top full-reference metrics.Comment: 10 pages, 4 figures, 6 tables, 1 supplementary materia
BASED: Benchmarking, Analysis, and Structural Estimation of Deblurring
This paper discusses the challenges of evaluating deblurring-methods quality
and proposes a reduced-reference metric based on machine learning. Traditional
quality-assessment metrics such as PSNR and SSIM are common for this task, but
not only do they correlate poorly with subjective assessments, they also
require ground-truth (GT) frames, which can be difficult to obtain in the case
of deblurring. To develop and evaluate our metric, we created a new motion-blur
dataset using a beam splitter. The setup captured various motion types using a
static camera, as most scenes in existing datasets include blur due to camera
motion. We also conducted two large subjective comparisons to aid in metric
development. Our resulting metric requires no GT frames, and it correlates well
with subjective human perception of blur