Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.

K. Lakhdhar

dblp:76/11154 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
0since 2021 · last 2012
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Image and video coding · 67% Audio and music processing · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Image and video coding › entropy coding
arithmetic coding
0.112012
Context-Based Adaptive Arithmetic Encoding of EAVQ Indices · IEEE Trans. Speech Audio Process. 2012
Audio and music processing
audio coding
0.112012
Context-Based Adaptive Arithmetic Encoding of EAVQ Indices · IEEE Trans. Speech Audio Process. 2012
Image and video coding
lossless compression
0.112012
Context-Based Adaptive Arithmetic Encoding of EAVQ Indices · IEEE Trans. Speech Audio Process. 2012

Methods — techniques the papers use, named apart from their topics

run-length encoding · 0.1context-based arithmetic encoding · 0.1
YearPublicationVenuePosition
2012 Context-Based Adaptive Arithmetic Encoding of EAVQ Indices
abstract
This paper presents a lossless compression algorithm for the binary indices of the embedded algebraic vector quantizer (EAVQ) used by the AMR-WB (Extended Adaptive Multi-Rate Wide Band) codec. We present a statical study of the EAVQ indices for diverse audio types (speech, music, etc.) and we discuss the design of the lossless algorithm including the choice of different strategies. The proposed algorithm combines run length encoding (RLE) and context-based arithmetic encoding to reduce the bitrate of the EAVQ indices by about 10% at the expense of 1% rise in complexity of the codec. The proposed algorithm can increase the segmental signal to noise ratio of about 9% at low rates for speech signals and improve the subjective scores in noisy channels by about 0.5 on a five-point scale if combined with an additional protection layer.
K. Lakhdhar, Roch Lefebvre
IEEE Trans. Speech Audio Process.1