16 papers · ranked by Valyu relevance
Alexander Engelsberger, Thomas Villmann, Rosario Lo Franco, GuiLu Long
'GuiLu Long'] In the field of machine learning, vector quantization is a category of low-complexity approaches that are nonetheless powerful for data representation and clustering or classification tasks. Vector quantization is based on the idea of representing a data or a class distribution using a small set of…
Verusca Severo, Felipe B. S. Ferreira, Rodrigo Spencer, Arthur Nascimento + 2 more
'Arthur Nascimento' 'Francisco Madeiro' 'Steve Vanlanduit'] Vector Quantization (VQ) is a technique with a wide range of applications. For example, it can be used for image compression. The codebook design for VQ has great significance in the quality of the quantized signals and can benefit from the use of swarm…
Liefu Ai, Yong Tao, Hongjun Cheng, Yuanzhi Wang + 3 more
'Deyang Liu' 'Xin Zheng'] To further improve the approximate nearest neighbor (ANN) search performance, an accumulative quantization (AQ) is proposed and applied to effective ANN search. It approximates a vector with the accumulation of several centroids, each of which is selected from a different codebook. To provide…
Muhammmad Bilal, Zahid Ullah, Omer Mujahid, Tama Fouzder + 1 more
Vector quantization (VQ) is a block coding method that is famous for its high compression ratio and simple encoder and decoder implementation. Linde-Buzo-Gray (LBG) is a renowned technique for VQ that uses a clustering-based approach for finding the optimum codebook. Numerous algorithms, such as Particle Swarm…
Edson Mata, Silvio Bandeira, Paulo de Mattos Neto, Waslon Lopes + 2 more
'Francisco Madeiro' 'Vittorio M. N. Passaro'] The performance of signal processing systems based on vector quantization depends on codebook design. In the image compression scenario, the quality of the reconstructed images depends on the codebooks used. In this paper, alternatives are proposed for accelerating families…
Lokesh Selvaraj, Balakrishnan Ganesan
Enhancing speech recognition is the primary intention of this work. In this paper a novel speech recognition method based on vector quantization and improved particle swarm optimization (IPSO) is suggested. The suggested methodology contains four stages, namely, (i) denoising, (ii) feature mining (iii), vector…
Piyush Kumar Pareek, Chethana Sridhar, R. Kalidoss, Muhammad Aslam + 3 more
Due to the increasing number of medical imaging images being utilized for the diagnosis and treatment of diseases, lossy or improper image compression has become more prevalent in recent years. The compression ratio and image quality, which are commonly quantified by PSNR values, are used to evaluate the performance of…
Eloundou Pascal Ntsama, Welba Colince, Pierre Ele
In this article, we make a comparative study for a new approach compression between discrete cosine transform (DCT) and discrete wavelet transform (DWT). We seek the transform proper to vector quantization to compress the EMG signals. To do this, we initially associated vector quantization and DCT, then vector…
Nobuhito Manome, Shuji Shinohara, Tatsuji Takahashi, Yu Chen + 1 more
'Ung-il Chung'] Human beings have adaptively rational cognitive biases for efficiently acquiring concepts from small-sized datasets. With such inductive biases, humans can generalize concepts by learning a small number of samples. By incorporating human cognitive biases into learning vector quantization (LVQ), a…
Kazuhisa Fujita, Muhammad Aleem
The growth of network-connected devices has led to an exponential increase in data generation, creating significant challenges for efficient data analysis. This data is generated continuously, creating a dynamic flow known as a data stream. The characteristics of a data stream may change dynamically, and this change is…
Pascal Lefevre, David Alleysson, Philippe Carre
In this paper, we address the problem of the use of a human visual system (HVS) model to improve watermark invisibility. We propose a new color watermarking algorithm based on the minimization of the perception of color differences. This algorithm is based on a psychovisual model of the dynamics of cone photoreceptors.…
Zoran Perić, Milan Savić, Nikola Simić, Bojan Denić + 2 more
'Vladimir Despotović' 'Friedhelm Schwenker'] Achieving real-time inference is one of the major issues in contemporary neural network applications, as complex algorithms are frequently being deployed to mobile devices that have constrained storage and computing power. Moving from a full-precision neural network model to…
Alin-Adrian Alecu, Mohammad Ali Tahouri, Adrian Munteanu, Bujor Păvăloiu + 1 more
Near-lossless coding schemes traditionally rely on uniform quantization to control the maximum absolute error ( $L_{\infty}$ norm) of residual signals, often assuming a parametric model for the source distribution. This paper introduces a novel design framework for non-uniform, entropy-aware $L_{\infty}$-oriented…
Roberto Leporini, Davide Pastorello
Optimal measurements for the discrimination of quantum states are useful tools for classification problems. In order to exploit the potential of quantum computers, feature vectors have to be encoded into quantum states represented by density operators. However, quantum-inspired classifiers based on nearest mean and on…
Ming-Te Wu, Zhaoqing Pan
In this study, a complexity-quality analysis with transcoding architectures is proposed for reducing inverse quantization numbers. This architecture is different from conventional transcoding scheme, which neglects the relationship between previous and current quantizer step size. However, the proposed transcoding…
Amine Zeguendry, Zahi Jarir, Mohamed Quafafou, Andreas Wichert
Despite its undeniable success, classical machine learning remains a resource-intensive process. Practical computational efforts for training state-of-the-art models can now only be handled by high speed computer hardware. As this trend is expected to continue, it should come as no surprise that an increasing number of…