Maximum Inner Product Search (MIPS) plays an important role in many applications ranging from information retrieval, recommender systems to natural language processing and machine learning. However, exhaustive MIPS is often expensive impractical when there are a large number of candidate items. The state-of-the-art approximated product quantization with score-aware loss, which weighs more heavi...

Self-organizing maps have been used extensively for condition-based maintenance, where quantization errors of test data referring to the self-organizing maps of healthy training data have been used as features. Researchers have used minimum quantization error as a health indicator, which is sensitive to noise in the training data. Some other researchers have used the average of the quantization...

Any quantization introduces errors. An important question is how to suppress their visual effect. In this paper we present a new quantization method for the geometry of 3D meshes, which enables aggressive quantization without significant loss of visual quality. Conventionally, quantization is applied directly to the 3-space coordinates. This form of quantization introduces high-frequency errors...

Any quantization introduces errors. An important question is how to suppress their visual effect. In this paper we present a new quantization method for the geometry of 3D meshes, which enables aggressive quantization without significant loss of visual quality. Conventionally, quantization is applied directly to the 3-space coordinates. This form of quantization introduces high-frequency errors...

We analyze the symbol error probability (SEP) of $M$-ary pulse amplitude modulation ($M$-PAM) receivers equipped with optimal low-resolution quantizers. first show that optimum detector can be reduced to a simple decision rule. Using this simplification, an exact SEP expression for quantized $M$-PAM is obtained when Nakagami-$m$ fading channel considered. The derived enables optimization quanti...

In this paper we study the use of an adaptive quantization step size, instead of a fixed one, for the Scalar Costa Scheme. We propose an adaptation method based on Weber’s law. This allows for a more effective embedding, which is also shown to render the watermark robust against sample value scaling. A model for the bit error probability due to the estimation of the adaptive quantization step s...

We propose a new algorithm for vector quantization:Average Competitive Learning Vector Quantization(ACLVQ). It is a rather simple modification of the classical Competitive Learning Vector Quantization(CLVQ). This new formulation gives us similar results for the quantization error to those obtained by the CLVQ and reduce considerably the computation time to achieve the optimal quantizer. We esta...

Vector Quantization is useful for data compression. Competitive Learning which minimizes reconstruction error is an appropriate algorithm for vector quantization of unlabelled data. Vector quantization of labelled data for classification has a different objective, to minimize the number of misclassifications, and a different algorithm is appropriate. We show that a variant of Kohonen’s LVQ2.1 a...

Traditional error di usion halftoning is a high quality method for producing binary images from digital grayscale images. Error di usion shapes the quantization noise power into the high frequency regions where the human eye is the least sensitive. Error di usion may be extended to color images by using error lters with matrix-valued coe cients to take into account the correlation among color p...

With a direct impact on compression performance, optimal quantization scheme is crucial for transformbased ECG data compression. However, traditional optimization schemes derived with signal adaption are commonly inherent with signal dependency and unsuitable for real-time application. In this paper, the variety of arrhythmia ECG signal is utilized for optimizing the quantization scheme of wave...