Year: 2014
Type: article
Abstract: This paper proposes a fast search algorithm for vector quantization (VQ) based on a binary search space (BSS-VQ). The trade-off and learning aspects (TLA) were used to enhance the line spectrum pair (LSP) encoder of the G.729 standard. In the trade-off aspect, a slight loss occurred in the quantization quality; however, substantial computational savings were achieved. In the learning aspect, the binary search space was developed using he learning process, which uses full search VQ (FSVQ) as an inferred function. In the experiment, computational savings of 86.19% and a quantization accuracy of 98.15% were achieved, which confirmed the excellent performance of the BSS-VQ approach.
Source: AASRI Procedia
Authors: Ning-Yun Ku, Shun-Chieh Chang, Sha-Hwa Hwang
Institution: National Taipei University of Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W1964813676