29,316 research outputs found
Adaptive Quantization Matrices for HD and UHD Display Resolutions in Scalable HEVC
HEVC contains an option to enable custom quantization matrices, which are
designed based on the Human Visual System and a 2D Contrast Sensitivity
Function. Visual Display Units, capable of displaying video data at High
Definition and Ultra HD display resolutions, are frequently utilized on a
global scale. Video compression artifacts that are present due to high levels
of quantization, which are typically inconspicuous in low display resolution
environments, are clearly visible on HD and UHD video data and VDUs. The
default QM technique in HEVC does not take into account the video data
resolution, nor does it take into consideration the associated display
resolution of a VDU to determine the appropriate levels of quantization
required to reduce unwanted video compression artifacts. Based on this fact, we
propose a novel, adaptive quantization matrix technique for the HEVC standard,
including Scalable HEVC. Our technique, which is based on a refinement of the
current HVS-CSF QM approach in HEVC, takes into consideration the display
resolution of the target VDU for the purpose of minimizing video compression
artifacts. In SHVC SHM 9.0, and compared with anchors, the proposed technique
yields important quality and coding improvements for the Random Access
configuration, with a maximum of 56.5% luma BD-Rate reductions in the
enhancement layer. Furthermore, compared with the default QMs and the Sony QMs,
our method yields encoding time reductions of 0.75% and 1.19%, respectively.Comment: Data Compression Conference 201
Mitigation of H.264 and H.265 Video Compression for Reliable PRNU Estimation
The photo-response non-uniformity (PRNU) is a distinctive image sensor
characteristic, and an imaging device inadvertently introduces its sensor's
PRNU into all media it captures. Therefore, the PRNU can be regarded as a
camera fingerprint and used for source attribution. The imaging pipeline in a
camera, however, involves various processing steps that are detrimental to PRNU
estimation. In the context of photographic images, these challenges are
successfully addressed and the method for estimating a sensor's PRNU pattern is
well established. However, various additional challenges related to generation
of videos remain largely untackled. With this perspective, this work introduces
methods to mitigate disruptive effects of widely deployed H.264 and H.265 video
compression standards on PRNU estimation. Our approach involves an intervention
in the decoding process to eliminate a filtering procedure applied at the
decoder to reduce blockiness. It also utilizes decoding parameters to develop a
weighting scheme and adjust the contribution of video frames at the macroblock
level to PRNU estimation process. Results obtained on videos captured by 28
cameras show that our approach increases the PRNU matching metric up to more
than five times over the conventional estimation method tailored for photos
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