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Record W2021042063 · doi:10.1145/2656203

ALP

2015· article· en· W2021042063 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueACM Transactions on Multimedia Computing Communications and Applications · 2015
Typearticle
Languageen
FieldComputer Science
TopicVideo Coding and Compression Technologies
Canadian institutionsSimon Fraser UniversityUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceVideoconferencingNetwork packetLossy compressionVideo qualityTeleconferenceKey (lock)Scheme (mathematics)Transmission (telecommunications)Computer networkPacket lossReal-time computingThe InternetLossless compressionMultimediaData compressionTelecommunicationsComputer securityArtificial intelligence

Abstract

fetched live from OpenAlex

There has been an increasing demand for interactive video transmission over the Internet for applications such as video conferencing, video calls, and telepresence applications. These applications are increasingly moving towards providing High Definition (HD) video quality to users. A key challenge in these applications is to preserve the quality of video when it is transported over best-effort networks that do not guarantee lossless transport of video packets. In such conditions, it is important to protect the transmitted video by using intelligent and adaptive protection schemes. Applications such as HD video conferencing require live interaction among participants, which limits the overall delay the system can tolerate. Therefore, the protection scheme should add little or no extra delay to video transport. We propose a novel Adaptive Loss Protection (ALP) scheme for interactive HD video applications such as video conferencing and video chats. This scheme adds negligible delay to the transmission process and is shown to achieve better quality than other schemes in lossy networks. The proposed ALP scheme adaptively applies four different protection modes to cope with the dynamic network conditions, which results in high video quality in all network conditions. Our ALP scheme consists of four protection modes ; each of these modes utilizes a protection method . Two of the modes rely on the state-of-the-art protection methods, and we propose a new Integrated Loss Protection (ILP) method for the other two modes. In the ILP method we integrate three factors for distributing the protection among packets. These three factors are error propagation, region of interest and header information. In order to decide when to switch between the protection modes, a new metric is proposed based on the effectiveness of each mode in performing protection, rather than just considering network statistics such as packet loss rate. Results show that by using this metric not only the overall quality will be improved but also the variance of quality will decrease. One of the main advantages of the proposed ALP scheme is that it does not increase the bit rate overhead in poor network conditions. Our results show a significant gain in video quality, up to 3dB PSNR improvement is achieved using our scheme, compared to protecting all packets equally with the same amount of overhead.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.980
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0030.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.067
GPT teacher head0.314
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it