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.

Method and apparatus for limiting data rate and image quality loss in lossy compression of sequences of digital images

Abstract: By limiting the extent to which the degree of quantization is lowered to increase the amount of compressed data, problems of data rate overshoots and image quality degradation in multi layer composites may be avoided. In particular, when a more complex image occurs after a simple image, the quantization used to compress the complex image will not cause as large of a change in the total amount of compressed data. Recovery from such a change also may occur more quickly. Where quantization tables are adjusted using a scaling factor, a limit on the scaling factor may be established such that the target data rate is not achieved for simple images. When rendering multi layer composites, this limit is such that recompression of previously compressed data does not result in additional loss of information. As a result, degradation of image quality in each layer of the composite is avoided. Where quantization tables are adjusted using a scaling factor, a limit on the scaling factor is established such that the same quantization table is used for each layer of the composite.

Robust high capacity data embedding

Abstract: We present a novel algorithm for high capacity data embedding The proposed algorithm gives high hiding ratios up to 1/13 (1 embedded bit out of 13 raw image pixels) subject to JPEG compression with quality factor equals 75 With such high capacity, one can easily embed some important regions of an image inside the image itself with very small perceptual distortion In the proposed algorithm, the DCT coefficients of 8/spl times/8 blocks are first rearranged using Hilbert curves Data is embedded using projections on a random orthogonal set The algorithm recovers the embedded data without any reference to the original image, with very low BER (around 01%) Finally, the proposed algorithm shows robustness to JPEG compression

An efficient Image Retrieval through DCT Histogram Quantization

Abstract: This paper proposes a new simple method of Discrete Cosine Transform (DCT) feature extraction that is used to accelerate the speed and decrease the storage needed in the image retrieving process. Image features are accessed and extracted directly from JPEG compressed domain. This method extracts and constructs a feature vector of histogram quantization from partial DCT coefficient in order to count the number of coefficients that have the same DCT coefficient over all image blocks. The database image and query image is equally divided into a non overlapping 8X8 block pixel, each of which is associated with a feature vector of histogram quantization derived directly from discrete cosine transform DCT. Users can select any query as the main theme of the query image. The retrieved images are those from the database that bear close resemblance with the query image and the similarity is ranked according to the closest similar measures computed by the Euclidean distance. The experimental results are significant and promising and show that our approach can easily identify main objects while to some extent reducing the influence of background in the image and in this way improves the performance of image retrieval.