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.

Wavelet-based Fragile Watermarking Scheme for Image Authentication

Abstract: We propose a fragile watermarking scheme in the wavelet transform domain that is sensitive to all kinds of manipulations and has the ability to localize the tampered regions. To achieve high transparency (i.e., low embedding distortion) while providing protection to all coefficients, the embedder involves all the coefficients within a hierarchical neighborhood of each sparsely selected watermarkable coefficient during the watermark embedding process. The way the nonwatermarkable coefficients are involved in the embedding process is content-dependent and nondeterministic, which allows the proposed scheme to put up resistance to the so-called vector quantization attack, Holliman-Memon attack, collage attack, and transplantation attack.

Document compression using rate-distortion optimized segmentation

Abstract: Effective document compression algorithms require that scanned document images be first segmented into regions such as text, pictures, and background. In this paper, we present a multilayer compression algorithm for document images. This compression al- gorithm first segments a scanned document image into different classes, then compresses each class using an algorithm specifically designed for that class. Two algorithms are investigated for seg- menting document images: a direct image segmentation algorithm called the trainable sequential MAP (TSMAP) segmentation algo- rithm, and a rate-distortion optimized segmentation (RDOS) algo- rithm. The RDOS algorithm works in a closed loop fashion by apply- ing each coding method to each region of the document and then selecting the method that yields the best rate-distortion trade-off. Compared with the TSMAP algorithm, the RDOS algorithm can of- ten result in a better rate-distortion trade-off, and produce more ro- bust segmentations by eliminating those misclassifications which can cause severe artifacts. At similar bit rates, the multilayer com- pression algorithm using RDOS can achieve a much higher subjec- tive quality than state-of-the-art compression algorithms, such as DjVu and SPIHT. © 2001 SPIE and IS&T. (DOI: 10.1117/1.1344590)

Hyperspectral Recovery from RGB Images using Gaussian Processes

Abstract: We propose to recover spectral details from RGB images of known spectral quantization by modeling natural spectra under Gaussian Processes and combining them with the RGB images. Our technique exploits Process Kernels to model the relative smoothness of reflectance spectra, and encourages non-negativity in the resulting signals for better estimation of the reflectance values. The Gaussian Processes are inferred in sets using clusters of spatio-spectrally correlated hyperspectral training patches. Each set is transformed to match the spectral quantization of the test RGB image. We extract overlapping patches from the RGB image and match them to the hyperspectral training patches by spectrally transforming the latter. The RGB patches are encoded over the transformed Gaussian Processes related to those hyperspectral patches and the resulting image is constructed by combining the codes with the original processes. Our approach infers the desired Gaussian Processes under a fully Bayesian model inspired by Beta-Bernoulli Process, for which we also present the inference procedure. A thorough evaluation using three hyperspectral datasets demonstrates the effective extraction of spectral details from RGB images by the proposed technique.

Efficient rate control techniques for video encoding

Abstract: This disclosure describes rate control techniques that can improve video encoding. In particular, the described rate control techniques exploit relationships between the number of bits encoded per frame and the number of non-zero coefficients of the video blocks after quantization. The number of number of non-zero coefficients of the video blocks after quantization is referred to as rho (ρ). The value of ρ is generally proportional to the number of bits used in the video encoding. This disclosure utilizes a relationship between ρ and a quantization parameter (QP) in order to achieve rate controlled video encoding. More specifically, this disclosure provides techniques for generating a lookup table (LUT) that maps values of ρ to different QPs.

Sequential pipelined processing for the compression and decompression of image data

Abstract: A sequential process-pipeline has a first processing stage (30) coupled to a CODEC (24) through a plurality of buffers, including an image data input buffer (28), an image data output buffer (26), and an address buffer (34). The image data input buffer stores, for each block of image data, control information for controlling the processing of an associated block of image data. The address buffer stores addresses, each of which identifies an initial address of a block of addresses within an image memory (22). A local controller (18) is responsive to the writing of an address into the address buffer to read the control information for a block to be processed, and to initiate the operation of the CODEC, in accordance with the read-out information, to execute a Discrete Cosine Transformation Process and a Discrete Cosine Transformation Quantization Process.