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.

Imaging apparatus, imaging circuit and imaging method

Abstract: An image by using a solid-state imaging sensor, the imaging apparatus including: a compression section configured to compress image data by dividing the image data into blocks each composed of same color component pixels adjacent to each other as a unit of compression; a memory used for temporarily storing compressed image data; a decompression section configured to decompress the compressed image data read out from the memory; and a signal processing section configured to carry out an image-quality correction process on decompressed image data, wherein each of the blocks is split in advance into two quantization-subject areas, block types are distinguished from each other by the position of the inter-area boundary between the two quantization-subject areas, and the compression section has a dynamic-range computation sub-section, a block-type select sub-section, and a quantization processing sub-section are provided.

SARI: self-authentication-and-recovery image watermarking system

Abstract: In this project, we designed a novel image authentication system based on a our semi-fragile watermarking technique. The system, called SARI, can accept quantization-based lossy compression to a determined degree without any false alarm and can sensitively detect and locate malicious manipulations. It's the first system that has such capability in distinguishing malicious attacks from acceptable operations. Furthermore, the corrupted area can be approximately recovered by the information hidden in the other part of the contentimage. The amount of information embedded in our SARI system has nearly reached the theoretical maximum zero-error information hiding capacity of digital images. The software prototype includes two parts - the watermark embedder that's freely distributed and the authenticator that can be deployed online as a third-party service or used in the recipient side.

An Efficient Nearest Neighbor Search Method

Abstract: A simple, but efficient, nearest neighbor search algorithm is proposed and simulation results demonstrating its effectiveness in the case of vector quantization for a given source are presented. The simulation results indicate that use of this approach reduces the number of multiplications and additions to as low as 9 percent of those required for the conventional full search method. The reduction in the number of subtractions is also considerable. The increase in the number of comparisons is moderate, and therefore, the total number of operations can be as low as 28 percent of those required by the full search method. An additional advantage of the described algorithm is the fact that it requires no precomputations and/or extra memory.

Lightweight Spectral–Spatial Squeeze-and- Excitation Residual Bag-of-Features Learning for Hyperspectral Classification

Abstract: Of late, convolutional neural networks (CNNs) find great attention in hyperspectral image (HSI) classification since deep CNNs exhibit commendable performance for computer vision-related areas. CNNs have already proved to be very effective feature extractors, especially for the classification of large data sets composed of 2-D images. However, due to the existence of noisy or correlated spectral bands in the spectral domain and nonuniform pixels in the spatial neighborhood, HSI classification results are often degraded and unacceptable. However, the elementary CNN models often find intrinsic representation of pattern directly when employed to explore the HSI in the spectral–spatial domain. In this article, we design an end-to-end spectral–spatial squeeze-and-excitation (SE) residual bag-of-feature ( S3EResBoF ) learning framework for HSI classification that takes as input raw 3-D image cubes without engineering and builds a codebook representation of transform feature by motivating the feature maps facilitating classification by suppressing useless feature maps based on patterns present in the feature maps. To boost the classification performance and learn the joint spatial–spectral features, every residual block is connected to every other 3-D convolutional layer through an identity mapping followed by an SE block, thereby facilitating the rich gradients through backpropagation. Additionally, we introduce batch normalization on every convolutional layer (ConvBN) to regularize the convergence of the network and scale invariant BoF quantization for the measure of classification. The experiments conducted using three well-known HSI data sets and compared with the state-of-the-art classification methods reveal that S3EResBoF provides competitive performance in terms of both classification and computation time.