84,956 research outputs found
A study on user preference of high dynamic range over low dynamic range video
The increased interest in High Dynamic Range (HDR) video over existing Low Dynamic Range (LDR) video during the last decade or so was primarily due to its inherent capability to capture, store and display the full range of real-world lighting visible to the human eye with increased precision. This has led to an inherent assumption that HDR video would be preferable by the end-user over LDR video due to the more immersive and realistic visual experience provided by HDR. This assumption has led to a considerable body of research into efficient capture, processing, storage and display of HDR video. Although, this is beneficial for scientific research and industrial purposes, very little research has been conducted in order to test the veracity of this assumption. In this paper, we conduct two subjective studies by means of a ranking and a rating based experiment where 60 participants in total, 30 in each experiment, were tasked to rank and rate several reference HDR video scenes along with three mapped LDR versions of each scene on an HDR display, in order of their viewing preference. Results suggest that given the option, end-users prefer the HDR representation of the scene over its LDR counterpart
Personalized Cinemagraphs using Semantic Understanding and Collaborative Learning
Cinemagraphs are a compelling way to convey dynamic aspects of a scene. In
these media, dynamic and still elements are juxtaposed to create an artistic
and narrative experience. Creating a high-quality, aesthetically pleasing
cinemagraph requires isolating objects in a semantically meaningful way and
then selecting good start times and looping periods for those objects to
minimize visual artifacts (such a tearing). To achieve this, we present a new
technique that uses object recognition and semantic segmentation as part of an
optimization method to automatically create cinemagraphs from videos that are
both visually appealing and semantically meaningful. Given a scene with
multiple objects, there are many cinemagraphs one could create. Our method
evaluates these multiple candidates and presents the best one, as determined by
a model trained to predict human preferences in a collaborative way. We
demonstrate the effectiveness of our approach with multiple results and a user
study.Comment: To appear in ICCV 2017. Total 17 pages including the supplementary
materia
The kindest cut: Enhancing the user experience of mobile tv through adequate zooming
The growing market of Mobile TV requires automated adaptation of standard TV footage to small size displays. Especially extreme long shots (XLS) depicting distant objects can spoil the user experience, e.g. in soccer content. Automated zooming schemes can improve the visual experience if the resulting footage meets user expectations in terms of the visual detail and quality but does not omit valuable context information. Current zooming schemes are ignorant of beneficial zoom ranges for a given target size when applied to standard definition TV footage. In two experiments 84 participants were able to switch between original and zoom enhanced soccer footage at three sizes - from 320x240 (QVGA) down to 176x144 (QCIF). Eye tracking and subjective ratings showed that zoom factors between 1.14 and 1.33 were preferred for all sizes. Interviews revealed that a zoom factor of 1.6 was too high for QVGA content due to low perceived video quality, but beneficial for QCIF size. The optimal zoom depended on the target display size. We include a function to compute the optimal zoom for XLS depending on the target device size. It can be applied in automatic content adaptation schemes and should stimulate further research on the requirements of different shot types in video coding
Combining Spot and Futures Markets: A Hybrid Market Approach to Dynamic Spectrum Access
Dynamic spectrum access is a new paradigm of secondary spectrum utilization
and sharing. It allows unlicensed secondary users (SUs) to exploit
opportunistically the under-utilized licensed spectrum. Market mechanism is a
widely-used promising means to regulate the consuming behaviours of users and,
hence, achieves the efficient allocation and consumption of limited resources.
In this paper, we propose and study a hybrid secondary spectrum market
consisting of both the futures market and the spot market, in which SUs
(buyers) purchase under-utilized licensed spectrum from a spectrum regulator,
either through predefined contracts via the futures market, or through spot
transactions via the spot market. We focus on the optimal spectrum allocation
among SUs in an exogenous hybrid market that maximizes the secondary spectrum
utilization efficiency. The problem is challenging due to the stochasticity and
asymmetry of network information. To solve this problem, we first derive an
off-line optimal allocation policy that maximizes the ex-ante expected spectrum
utilization efficiency based on the stochastic distribution of network
information. We then propose an on-line VickreyCClarkeCGroves (VCG) auction
that determines the real-time allocation and pricing of every spectrum based on
the realized network information and the pre-derived off-line policy. We
further show that with the spatial frequency reuse, the proposed VCG auction is
NP-hard; hence, it is not suitable for on-line implementation, especially in a
large-scale market. To this end, we propose a heuristics approach based on an
on-line VCG-like mechanism with polynomial-time complexity, and further
characterize the corresponding performance loss bound analytically. We finally
provide extensive numerical results to evaluate the performance of the proposed
solutions.Comment: This manuscript is the complete technical report for the journal
version published in INFORMS Operations Researc
Recommended from our members
Zapping index: Using smile to measure advertisement zapping likelihood
In marketing and advertising research, 'zapping' is defined as the action when a viewer stops watching a commercial. Researchers analyze users' behavior in order to prevent zapping which helps advertisers to design effective commercials. Since emotions can be used to engage consumers, in this paper, we leverage automated facial expression analysis to understand consumers' zapping behavior. Firstly, we provide an accurate moment-to-moment smile detection algorithm. Secondly, we formulate a binary classification problem (zapping/non-zapping) based on real-world scenarios, and adopt smile response as the feature to predict zapping. Thirdly, to cope with the lack of a metric in advertising evaluation, we propose a new metric called Zapping Index (ZI). ZI is a moment-to-moment measurement of a user's zapping probability. It gauges not only the reaction of a user, but also the preference of a user to commercials. Finally, extensive experiments are performed to provide insights and we make recommendations that will be useful to both advertisers and advertisement publishers
- …