3,865 research outputs found
Review of Person Re-identification Techniques
Person re-identification across different surveillance cameras with disjoint
fields of view has become one of the most interesting and challenging subjects
in the area of intelligent video surveillance. Although several methods have
been developed and proposed, certain limitations and unresolved issues remain.
In all of the existing re-identification approaches, feature vectors are
extracted from segmented still images or video frames. Different similarity or
dissimilarity measures have been applied to these vectors. Some methods have
used simple constant metrics, whereas others have utilised models to obtain
optimised metrics. Some have created models based on local colour or texture
information, and others have built models based on the gait of people. In
general, the main objective of all these approaches is to achieve a
higher-accuracy rate and lowercomputational costs. This study summarises
several developments in recent literature and discusses the various available
methods used in person re-identification. Specifically, their advantages and
disadvantages are mentioned and compared.Comment: Published 201
Recent Advances in Image Restoration with Applications to Real World Problems
In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included
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
Video Compression and Optimization Technologies - Review
The use of video streaming is constantly increasing. High-resolution video requires resources on both the sender and the receiver side. There are many compression techniques that can be utilized to compress the video and simultaneously maintain quality. The main goal of this paper is to provide an overview of video streaming and QoE. This paper describes the basic concepts and discusses existing methodologies to measure QoE. Subjective, objective, and video compression technologies are discussed. This review paper gathers the codec implementation developed by MPEG, Google, and Apple. This paper outlines the challenges and future research directions that should be considered in the measurement and assessment of quality of experience for video services
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