Our special session proposal in ACM International Conference on Distributed Smart Cameras (ICDSC’19) has been accepted!
The special session title is “Trends in Free Navigation Technologies”.
Find here the call for papers
It is an open special session, so you can apply directly through the conference website.
Telecom ParisTech, a CS/EE school of Institut Polytechnique de Paris, is hiring an Associate Professor in Video Analysis and Learning. The position will be located in the Multimedia Team, within the Image, Data, Signal Department (IDS), and the LTCI laboratory.
The Multimedia team has a long activity in the domain of video and image coding and transmission. More recently, video analysis and learning activity have become more and more relevant for the team who runs now a regional study group about Machine and Deep Learning applications to Image and Video compression. The team has the target to expand its activity in this area, and several new and exciting research projects have just been launched, such as research programs in Deep Learning assisted video compression and Learning-based photographic quality evaluation. In this context, and to support the increasing activity of the team, a permanent position in video analysis and learning has been opened.
Applicants are expected to provide an outstanding academic research record and will be encouraged to advise PhD theses, supervise engineers and post-docs, while being actively involved in funded projects and in the activities of the Multimedia team. The teaching activities will take place in the engineer and master tracks at Telecom ParisTech and can be given in English.
Find here more information.
Three articles have been accepted into IEEE ICASSP :
1) S. Zheng, M. Cagnazzo, M. Kieffer. “CHANNEL IMPULSIVE NOISE MITIGATION FOR LINEAR VIDEO CODING SCHEMES”
2) L. Wang, A. Fiandrotti, A. Purica, G. Valenzise, M. Cagnazzo. “ENHANCING HEVC SPATIAL PREDICTION BY CONTEXT-BASED LEARNING”
3) P. Nikitin, M. Cagnazzo, J. Jung. “COMPRESSION IMPROVEMENT VIA REFERENCE ORGANIZATION FOR 2D-MULTIVIEW CONTENT”.
Congrats to Shuo, Li and Pavel.
Shuo Zheng’s PhD defense will take place at 5th February, 10 am, Amphi Opale at TélécomParisTech (46 rue Barrault, 75013 Paris).
Committee:
Title: Accounting for Channel Constraints in Joint Source-Channel Video Coding Schemes
Abstract: SoftCast based Linear Video Coding (LVC) schemes have been emerged in the last decade as a quasi analog joint-source-channel alternative to classical video coding schemes. Theoretical analyses have shown that analog coding is better than digital coding in a multicast scenario when the channel signal-to-noise ratios (C-SNR) dier among receivers. LVC schemes provide in such context a decoded video quality at dierent receivers proportional to their C-SNR. This thesis considers rst the channel precoding and decoding matrix design problem for LVC schemes under a per-subchannel power constraint. Such constraint is found, e.g., on Power Line Telecommunication (PLT) channels and is similar to per-antenna power constraints in multi-antenna transmission system. An optimal design approach is proposed, involving a multi-level water lling algorithm and the solution of a structured Hermitian Inverse Eigenvalue problem. Three lower-complexity alternative suboptimal algorithms are also proposed. Extensive experiments show that the suboptimal algorithms perform closely to the optimal one and can reduce signicantly the complexity. The precoding matrix design in multicast situations also has been considered. A second main contribution consists in an impulse noise mitigation approach for LVC schemes. Impulse noise identication and correction can be formulated as a sparse vector recovery problem. A Fast Bayesian Matching Pursuit (FBMP) algorithm is adapted to LVC schemes. Subchannels provisioning for impulse noise mitigation is necessary, leading to a nominal video quality decrease in absence of impulse noise. A phenomenological model (PM) is proposed to describe the impulse noise correction residual. Using the PM model, an algorithm to evaluate the optimal number of subchannels to provision is proposed. Simulation results show that the proposed algorithms signicantly improve the video quality when transmitted over channels prone to impulse noise.
Our article entitled “Very Low Bitrate Semantic Compression of Airplane Cockpit Screen Content” has been accepted for publication in IEEE Transactions on Multimedia.
Congratulations to Iulia, our first author.
I presented the team activity report in the context of the HCERES evaluation of the lab.
I have been promoted to Full Professor (starting from Dec 1st 2018)
“Quality assessment of deep-learning based image compression”
Sorry, this entry is only available in French.