Results 301 to 310 of about 1,866,673 (365)

Autoregressive Video Generation without Vector Quantization

arXiv.org
This paper presents a novel approach that enables autoregressive video generation with high efficiency. We propose to reformulate the video generation problem as a non-quantized autoregressive modeling of temporal frame-by-frame prediction and spatial ...
Haoge Deng, Ting Pan, Haiwen Diao, Zhengxiong Luo, Yufeng Cui, Huchuan Lu, Shiguang Shan, Yonggang Qi, Xinlong Wang   +8 more
semanticscholar   +1 more source

Restructuring Vector Quantization with the Rotation Trick

International Conference on Learning Representations
Vector Quantized Variational AutoEncoders (VQ-VAEs) are designed to compress a continuous input to a discrete latent space and reconstruct it with minimal distortion.
Christopher Fifty, Ronald G. Junkins, Dennis Duan, Aniketh Iger, Jerry Liu, Ehsan Amid, Sebastian Thrun, Christopher R'e   +7 more
semanticscholar   +1 more source

Learning Vector Quantization [PDF]

, 1995
Closely related to VQ and SOM is Learning Vector Quantization (LVQ). This name signifies a class of related algorithms, such as LVQ1, LVQ2, LVQ3, and OLVQ1. While VQ and the basic SOM are unsupervised clustering and learning methods, LVQ describes supervised learning.
openaire   +1 more source

Convergence of Vector Quantizers with Applications to Optimal Quantization

SIAM Journal on Applied Mathematics, 1984
Summary: Suppose that a sequence of probability distribution functions \(\{F_ n\}\) converges weakly to a distribution function F. Does the sequence of optimal quantizers for the \(F_ n's\) converge to an optimal quantizer for F? If so, do the respective distortions converge to the optimal distortion for F?
Gary L. Wise, Efren F. Abaya
openaire   +2 more sources

A pyramid vector quantizer

IEEE Transactions on Information Theory, 1986
The geometric properties of a memoryless Laplacian source are presented and used to establish a source coding theorem. Motivated by this geometric structure, a pyramid vector quantizer (PVQ) is developed for arbitrary vector dimension. The PVQ is based on the cubic lattice points that lie on the surface of an L-dimensional pyramid and has simple ...
openaire   +3 more sources

A vector quantizer for image restoration

Proceedings of 3rd IEEE International Conference on Image Processing, 1998
This paper presents a novel technique for image restoration based on nonlinear interpolative vector quantization (NLIVQ). The algorithm performs nonlinear restoration of diffraction-limited images concurrently with quantization. It is trained on image pairs consisting of an original image and its diffraction-limited counterpart.
Mariappan S. Nadar, Bobby R. Hunt, Michael W. Marcellin, Ali Bilgin, D.G. Sheppard   +4 more
openaire   +3 more sources

Interframe hierarchical vector quantization

International Conference on Acoustics, Speech, and Signal Processing, 1989
An interframe hierarchical vector quantizer (IHVQ) is presented that is capable of encoding image sequence scenes at rates below 0.3 bit per pixel per frame. A regular decomposition quadtree method is used to segment the interframe differential signal into homogeneous regions of different block size.
Yushu Feng, Shihkuan E. Lin, Nasser M. Nasrabadi   +2 more
openaire   +3 more sources

Vector quantization with complexity costs

IEEE Transactions on Information Theory, 1993
Summary: Vector quantization is a data compression method where a set of data points is encoded by a reduced set of reference vectors, the codebook. A vector quantization strategy is discussed that jointly optimizes distortion errors and the codebook complexity, thereby determining the size of the codebook.
Joachim M. Buhmann, Hans Kühnel
openaire   +2 more sources

Bilinear vector quantization [PDF]

2013 IEEE Digital Signal Processing and Signal Processing Education Meeting (DSP/SPE), 2013
Vector quantization (VQ) is a simple and useful data compression algorithm which has been widely applied in many fields such as image processing and pattern recognition. Because each data block is encoded by only one approximate vector in the codebook, the accuracy of the reconstructed blocks is usually poor in VQ.
Jianji Wang, Qian Zhang, Zhuoran Li
openaire   +1 more source

Vector quantization of the articulatory space

IEEE Transactions on Acoustics, Speech, and Signal Processing, 1988
A technique is presented for quantizing the articulatory space, i.e. for replacing the continuum of all possible vocal tract shapes by a finite set of shapes that span the articulatory space. Vocal tract shapes are represented as vectors of parameters that control an articulatory model, so vector quantization are applicable for deriving this codebook ...
J. N. Larar, J. Schroeter, Man Mohan Sondhi   +2 more
openaire   +3 more sources