One strategy was compression ultrasonography, followed by anticoagulation in case of positive results (high probability ventilation-perfusion (VQ) scans) or by VQ scan or spiral computed tomography (CT) in case of negative results. Low probability VQ scans resulted in no treatment, whereas high probability VQ scans resulted in anticoagulation. Intermediate tests were followed by a second test (...

Full-search vector quantization (VQ) provides optimal results only with high memory and computational cost. We describe the computational and memory requirements of treestructured VQ, residual VQ (RVQ), and tree-structured RVQ. We present multiple-rate, adaptive-search implementations of these VQ structures, and simulation results with video sequences. Tree-structured RVQ provides up to 1.5 db ...

One strategy was compression ultrasonography, followed by anticoagulation in case of positive results (high probability ventilation-perfusion (VQ) scans) or by VQ scan or spiral computed tomography (CT) in case of negative results. Low probability VQ scans resulted in no treatment, whereas high probability VQ scans resulted in anticoagulation. Intermediate tests were followed by a second test (...

In this study, the subclasses Vq,ϱℓ(c

We present a floating-gate based system for computing vector quantization (VQ), which is typically used for data compression and classification of signals to symbols. We present an architecture and resulting circuits which will enable direct programming / storage of weight vectors, as well as methods for adaptive VQ. We use an analog bump circuit to perform a continuous distance computation alo...

Vector Quantization (VQ) is a clustering problem in the fields of signal processing, source coding, information theory etc. Taking advantage recent advances field deep neural networks, this paper investigates performance between VQ problems and networks. A k-means-based network architecture for presented to solve problems. By applying learning implementation convergence optimization, (algorithm...

Because classical fast vector quantization(VQ) algorithms can't be used in the LSF vector quantizers that use varying weighted Euclidean distance, a novel fast VQ search algorithm —CRVQCS (Constrained Range Vector Quantization based on Component Searching) is presented in this paper. The CRVQ-CS algorithm works well with the varying weighted Euclidean distance and yields the same result as full...

A new Constrained Storage VQ (CSVQ) structure based on linear transforms and translates of a common root codebook is proposed. The new VQ structure, named LT-CSVQ (Linear Translate CSVQ), acts as a building block for multistage VQ implementations (LT-CS-MSVQ), and significantly reduces storage requirements from that required in tree-multistage VQ implementations. LT-CS-MSVQ is most appropriate ...

The authors introduce a novel coding technique which significantly improves the performance of the traditional vector quantisation (VQ) schemes at low bit rates. High interblock correlation in natural images results in a high probability that neighbouring image blocks are mapped to small subsets of the VQ codebook, which contains highly correlated codevectors. If, instead of the whole VQ codebo...