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.

DCT coefficient distributions

Abstract: Many image and video compression schemes perform the discrete cosine transform (DCT) to represent image data in frequency space. An analysis of a broad suite of images confirms previous findings that a Laplacian distribution can be used to model the luminance ac coefficients. This model is expanded and applied to color space (Cr/Cb) coefficients. In MPEG, the DCT is used to code interframe prediction error terms. The distribution of these coefficients is explored. Finally, the distribution model is applied to improve dynamic generation of quantization matrices.

High-definition video encoding system having color-sensitive quantization

Abstract: A video signal encoder uses an encoding system such as that developed by the Moving Picture Experts Group (MPEG). A key component of this encoding system increases the quantization step size of image data, thus decreasing its quantization resolution, to reduce the number of bits used to encode the data. Apparatus according to the present invention, monitors the color information being encoded and inhibits or reduces any increase in quantization step size for the luminance component and the color component of the image when signals representing saturated or nearly saturated red image components are being encoded.

Enhancing Sketch-Based Image Retrieval by Re-Ranking and Relevance Feedback

Abstract: A sketch-based image retrieval often needs to optimize the tradeoff between efficiency and precision. Index structures are typically applied to large-scale databases to realize efficient retrievals. However, the performance can be affected by quantization errors. Moreover, the ambiguousness of user-provided examples may also degrade the performance, when compared with traditional image retrieval methods. Sketch-based image retrieval systems that preserve the index structure are challenging. In this paper, we propose an effective sketch-based image retrieval approach with re-ranking and relevance feedback schemes. Our approach makes full use of the semantics in query sketches and the top ranked images of the initial results. We also apply relevance feedback to find more relevant images for the input query sketch. The integration of the two schemes results in mutual benefits and improves the performance of the sketch-based image retrieval.

Color Image Quantization: A Short Review and an Application with Artificial Bee Colony Algorithm

Abstract: Color quantization is the process of reducing the number of colors in a digital image. The main objective of quantization process is that significant information should be preserved while reducing the color of an image. In other words, quantization process shouldn't cause significant information loss in the image. In this paper, a short review of color quantization is presented and a new color quantization method based on artificial bee colony algorithm (ABC) is proposed. The performance of the proposed method is evaluated by comparing it with the performance of the most widely used quantization methods such as K-means, Fuzzy C Means (FCM), minimum variance and particle swarm optimization (PSO). The obtained results indicate that the proposed method is superior to the others.

Quantization based multiple medical information watermarking for secure e-health

Abstract: In this paper, an improved wavelet based medical image watermarking algorithm is proposed. Initially, the proposed technique decomposes the cover medical image into ROI and NROI regions and embedding three different watermarks into the non-region of interest (NROI) part of the transformed DWT cover image for compact and secure medical data transmission in E-health environment. In addition, the method addressing the problem of channel noise distortion may lead to faulty watermark by applying error correcting codes (ECCs) before embedding them into the cover image. Further, the bit error rates (BER) performance of the proposed method is determined for different kind of attacks including ‘Checkmark’ attacks. Experimental results indicate that the Turbo code performs better than BCH (Bose-Chaudhuri-Hochquenghem) error correction code. Furthermore, the experimental results validate the effectiveness of the proposed framework in terms of BER and embedding capacity compared to other state-of-the-art methods. Therefore, the proposed method finds potential application in prevention of patient identity theft in e-health applications.