We develop a two stage split vector quantization method with optimum bit allocation, for achieving minimum computational complexity. This also results in much lower memory requirement than the recently proposed switched split vector quantization method. To improve the rate-distortion performance further, a region specific normalization is introduced, which results in 1 bit/vector improvement ov...

This report presents some results and findings of our work on very-lowbit-rate video compression systems using vector quantization (VQ). We have identified multiscale segmentation and variable-rate coding as two important concepts whose effective use can lead to superior compression performance. Two VQ algorithms that attempt to use these two aspects are presented: one based on residual vector ...

With the purpose of learning and utilizing explicit dense topic embeddings, we propose three variations novel vector-quantization-based models (VQ-TMs): (1) Hard VQ-TM, (2) Soft (3) Multi-View VQ-TM. The model family capitalize on vector quantization techniques, embedded input documents, viewing words as mixtures topics. Guided by a comprehensive set evaluation metrics, conduct systematic quant...

This thesis studies the multiple description vector quantization with lattice codebooks (MDLVQ). The design of index assignment is crucial to the performance of MDLVQ. However, to our best knowledge, none of previous index assignment algorithms for MDLVQ is optimal. In this thesis, we propose a simple linear-time index assignment algorithm for MDLVQ with any K ≥ 2 balanced descriptions. We prov...

In this paper a novel type of gain-shape vector quantization (GSVQ) is presented, denoted as Logarithmic Cubic Vector Quantization (LCVQ). LCVQ is based on a decomposition of the vector to be quantized into a gain factor and a shape vector which is a normalized version of the input vector. Both components are quantized independently and transmitted to the decoder. Compared to other GSVQ approac...