Quantization (image processing)

About: Quantization (image processing) is a research topic. Over the lifetime, 7977 publications have been published within this topic receiving 126632 citations.

A hand gesture recognition system and method

Abstract: Noise problems in processing small images or large-granularity images are reduced by representing hand images as rotational vectors calculated using a real-valued centroid. The hand region is therefore sectored independently of pixel quantization. Color segmentation is used to identify hand-color regions, followed by region labelling to filter out noise regions based on region size. Principal component analysis is used to plot gesture models.

The LOT: transform coding without blocking effects

Abstract: An exact derivation of an optimal lapped orthogonal transform (LOT) is presented. The optimal LOT is related to the discrete cosine transform (DCT) in such a way that a fast algorithm for a nearly optimal LOT is derived. Compared to the DCT, the fast LOT requires about 20-30% more computations, mostly additions. An image coding example demonstrates the effectiveness of the LOT in reducing blocking effects; the LOT actually leads to slightly smaller signal reconstruction errors than does the DCT. >

Iterative procedures for reduction of blocking effects in transform image coding

Abstract: The authors propose an iterative block reduction technique based on the theory of a projection onto convex sets. The idea is to impose a number of constraints on the coded image in such a way as to restore it to its original artifact-free form. One such constraint can be derived by exploiting the fact that the transform-coded image suffering from blocking effects contains high-frequency vertical and horizontal artifacts corresponding to vertical and horizontal discontinuities across boundaries of neighboring blocks. Another constraint has to be with the quantization intervals of the transform coefficients. Specifically, the decision levels associated with transform coefficient quantizers can be used as lower and upper bounds on transform coefficients, which in turn define boundaries of the convex set for projection. A few examples of the proposed approach are presented. >

Fast image-based localization using direct 2D-to-3D matching

Abstract: Recently developed Structure from Motion (SfM) reconstruction approaches enable the creation of large scale 3D models of urban scenes. These compact scene representations can then be used for accurate image-based localization, creating the need for localization approaches that are able to efficiently handle such large amounts of data. An important bottleneck is the computation of 2D-to-3D correspondences required for pose estimation. Current stateof- the-art approaches use indirect matching techniques to accelerate this search. In this paper we demonstrate that direct 2D-to-3D matching methods have a considerable potential for improving registration performance. We derive a direct matching framework based on visual vocabulary quantization and a prioritized correspondence search. Through extensive experiments, we show that our framework efficiently handles large datasets and outperforms current state-of-the-art methods.

Color image quantization for frame buffer display

Abstract: Algorithms for adaptive, tapered quantization of color images are described. The research is motivated by the desire to display high-quality reproductions of color images with small frame buffers. ...