Vector quantization and signal compression
Vector quantization and signal compression
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vector quantization based on genetic simulated annealing
Signal Processing
An Anomaly Intrusion Detection System Based on Vector Quantization
IEICE - Transactions on Information and Systems
Hadamard transform based fast codeword search algorithm for high-dimensional VQ encoding
Information Sciences: an International Journal
Information Sciences: an International Journal
Fast VQ codebook search algorithm for grayscale image coding
Image and Vision Computing
Improved batch fuzzy learning vector quantization for image compression
Information Sciences: an International Journal
Adaptive embedding techniques for VQ-compressed images
Information Sciences: an International Journal
A path optional lossless data hiding scheme based on VQ joint neighboring coding
Information Sciences: an International Journal
Compression of 3-D Point Visual Data Using Vector Quantization and Rate-Distortion Optimization
IEEE Transactions on Multimedia
The JPEG2000 still image coding system: an overview
IEEE Transactions on Consumer Electronics
The JPEG still picture compression standard
IEEE Transactions on Consumer Electronics
IEEE Transactions on Image Processing
Traffic sign recognition using group sparse coding
Information Sciences: an International Journal
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Vector quantization (VQ), a lossy image compression, is widely used for many applications due to its simple architecture, fast decoding ability, and high compression rate. Traditionally, VQ applies the full search algorithm to search for the codeword that best matches each image vector in the encoding procedure. However, matching in this manner consumes a lot of computation time and leads to a heavy burden for the VQ method. Therefore, Torres and Huguet proposed a double test algorithm to improve the matching efficiency. However, their scheme does not include an initiation strategy to choose an initially searched codeword for each image vector, and, as a result, matching efficiency may be affected significantly. To overcome this drawback, we propose an improved double test scheme with a fine initialization as well as a suitable search order. Our experimental results indicate that the computation time of the double test algorithm can be significantly reduced by the proposed method. In addition, the proposed method is more flexible than existing schemes.