An iterative joint codebook and classifier improvement algorithm for finite-state vector quantization

A finite-state vector quantizer (FSVQ) is a multicodebook system in, which the current state (or codebook) is chosen as a function of the previously quantized vectors. The authors introduce a novel iterative algorithm for joint codebook and next state function design of full search finite-state vector quantizers. They consider the fixed-rate case, for which no optimal design strategy is known. A locally optimal set of codebooks is designed for the training data and then predecessors to the training vectors associated with each codebook are appropriately labelled and used in designing the classifier. The algorithm iterates between next state function and state codebook design until it arrives at a suitable solution. The proposed design consistently yields better performance than the traditional FSVQ design method (under identical state space and codebook constraints)

An Efficient Coding Method for Teleconferencing Video and Confocal Microscopic Image Sequences

In this paper we propose a three-dimensional vector quantization based video coding scheme. The algorithm uses a 3D vector quantization pyramidal code book based model with adaptive code book pyramidal codebook for compression. The pyramidal code book based model helps in getting high compression in case of modest motion. The adaptive vector quantization algorithm is used to train the code book for optimal performance with time. Some of the distinguished features of our algorithm are its excellent performance due to its adaptive behavior to the video composition and excellent compression due to codebook approach. We also propose an efficient codebook based post processing technique which enables the vector quantizer to possess higher correlation preservation property. Based on the special pattern of the codebook imposed by post-processing technique, a window based fast search (WBFS) algorithm is proposed. The WBFS algorithm not only accelerates the vector quantization processing, but also results in better rate-distortion performance. The proposed approach can be used for both teleconferencing videos and to compress images obtained from confocal laser scanning microscopy (CLSM). The results show that the proposed method gave higher subjective and objective image quality of reconstructed images at a better compression ratio and presented more acceptable results when applying image processing filters such as edge detection on reconstructed images. The experimental results demonstrate that the proposed method outperforms the teleconferencing compression standards H.261 and LBG based vector quantization technique

Efficient Large-scale Approximate Nearest Neighbor Search on the GPU

We present a new approach for efficient approximate nearest neighbor (ANN) search in high dimensional spaces, extending the idea of Product Quantization. We propose a two-level product and vector quantization tree that reduces the number of vector comparisons required during tree traversal. Our approach also includes a novel highly parallelizable re-ranking method for candidate vectors by efficiently reusing already computed intermediate values. Due to its small memory footprint during traversal, the method lends itself to an efficient, parallel GPU implementation. This Product Quantization Tree (PQT) approach significantly outperforms recent state of the art methods for high dimensional nearest neighbor queries on standard reference datasets. Ours is the first work that demonstrates GPU performance superior to CPU performance on high dimensional, large scale ANN problems in time-critical real-world applications, like loop-closing in videos

Progressive Vector Quantization on a massively parallel SIMD machine with application to multispectral image data

A progressive vector quantization (VQ) compression approach is discussed which decomposes image data into a number of levels using full search VQ. The final level is losslessly compressed, enabling lossless reconstruction. The computational difficulties are addressed by implementation on a massively parallel SIMD machine. We demonstrate progressive VQ on multispectral imagery obtained from the Advanced Very High Resolution Radiometer instrument and other Earth observation image data, and investigate the trade-offs in selecting the number of decomposition levels and codebook training method

Efficient and Robust Detection of Duplicate Videos in a Database

In this paper, the duplicate detection method is to retrieve the best matching model video for a given query video using fingerprint. We have used the Color Layout Descriptor method and Opponent Color Space to extract feature from frame and perform k-means based clustering to generate fingerprints which are further encoded by Vector Quantization. The model-to-query video distance is computed using a new distance measure to find the similarity. To perform efficient search coarse-to-fine matching scheme is used to retrieve best match. We perform experiments on query videos and real time video with an average duration of 60 sec; the duplicate video is detected with high similarity

DiskANN++: Efficient Page-based Search over Isomorphic Mapped Graph Index using Query-sensitivity Entry Vertex

Given a vector dataset $\mathcal{X}$ and a query vector $\vec{x}_q$, graph-based Approximate Nearest Neighbor Search (ANNS) aims to build a graph index $G$ and approximately return vectors with minimum distances to $\vec{x}_q$ by searching over $G$. The main drawback of graph-based ANNS is that a graph index would be too large to fit into the memory especially for a large-scale $\mathcal{X}$. To solve this, a Product Quantization (PQ)-based hybrid method called DiskANN is proposed to store a low-dimensional PQ index in memory and retain a graph index in SSD, thus reducing memory overhead while ensuring a high search accuracy. However, it suffers from two I/O issues that significantly affect the overall efficiency: (1) long routing path from an entry vertex to the query's neighborhood that results in large number of I/O requests and (2) redundant I/O requests during the routing process. We propose an optimized DiskANN++ to overcome above issues. Specifically, for the first issue, we present a query-sensitive entry vertex selection strategy to replace DiskANN's static graph-central entry vertex by a dynamically determined entry vertex that is close to the query. For the second I/O issue, we present an isomorphic mapping on DiskANN's graph index to optimize the SSD layout and propose an asynchronously optimized Pagesearch based on the optimized SSD layout as an alternative to DiskANN's beamsearch. Comprehensive experimental studies on eight real-world datasets demonstrate our DiskANN++'s superiority on efficiency. We achieve a notable 1.5 X to 2.2 X improvement on QPS compared to DiskANN, given the same accuracy constraint.Comment: 15 pages including reference

Learning to compress and search visual data in large-scale systems

The problem of high-dimensional and large-scale representation of visual data is addressed from an unsupervised learning perspective. The emphasis is put on discrete representations, where the description length can be measured in bits and hence the model capacity can be controlled. The algorithmic infrastructure is developed based on the synthesis and analysis prior models whose rate-distortion properties, as well as capacity vs. sample complexity trade-offs are carefully optimized. These models are then extended to multi-layers, namely the RRQ and the ML-STC frameworks, where the latter is further evolved as a powerful deep neural network architecture with fast and sample-efficient training and discrete representations. For the developed algorithms, three important applications are developed. First, the problem of large-scale similarity search in retrieval systems is addressed, where a double-stage solution is proposed leading to faster query times and shorter database storage. Second, the problem of learned image compression is targeted, where the proposed models can capture more redundancies from the training images than the conventional compression codecs. Finally, the proposed algorithms are used to solve ill-posed inverse problems. In particular, the problems of image denoising and compressive sensing are addressed with promising results.Comment: PhD thesis dissertatio

Image Compression Using Subband Wavelet Decomposition and DCT-based Quantization

International audienceThe aim of this work is to evaluate the performance of an image compression system based on wavelet-based subband decomposition. The compression method used in this paper differs from the classical procedure in the direction where the scalar quantization of the coarse scale approximation sub-image is replaced by a discrete cosine transform (DCT) based quantization. The images were decomposed using wavelet filters into a set of subbands with different resolutions corresponding to different frequency bands. The resulting high frequency subbands were vector quantized according to the magnitude of their variances. The coarse scale approximation sub-image is quantized using scalar quantization and then using DCT base quantization to show the benefit of this new optional method in term of CPU computationa1 cost vs restitution quality

A vector quantization approach to universal noiseless coding and quantization

A two-stage code is a block code in which each block of data is coded in two stages: the first stage codes the identity of a block code among a collection of codes, and the second stage codes the data using the identified code. The collection of codes may be noiseless codes, fixed-rate quantizers, or variable-rate quantizers. We take a vector quantization approach to two-stage coding, in which the first stage code can be regarded as a vector quantizer that “quantizes” the input data of length n to one of a fixed collection of block codes. We apply the generalized Lloyd algorithm to the first-stage quantizer, using induced measures of rate and distortion, to design locally optimal two-stage codes. On a source of medical images, two-stage variable-rate vector quantizers designed in this way outperform standard (one-stage) fixed-rate vector quantizers by over 9 dB. The tail of the operational distortion-rate function of the first-stage quantizer determines the optimal rate of convergence of the redundancy of a universal sequence of two-stage codes. We show that there exist two-stage universal noiseless codes, fixed-rate quantizers, and variable-rate quantizers whose per-letter rate and distortion redundancies converge to zero as (k/2)n -1 log n, when the universe of sources has finite dimension k. This extends the achievability part of Rissanen's theorem from universal noiseless codes to universal quantizers. Further, we show that the redundancies converge as O(n-1) when the universe of sources is countable, and as O(n-1+ϵ) when the universe of sources is infinite-dimensional, under appropriate conditions