Topic
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.
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14 May 2006
TL;DR: Several approaches to incorporating signal processing tasks such as statistical classification, estimation, and modeling into the quantization and possible extensions to distributed signal processing are surveyed.
Abstract: Quantization is the mapping of continuous quantities into discrete quantities, an operation far more general and flexible than the ubiquitous example of analog-to-digital conversion of scalar amplitude values. By appropriate choice of distortion measures and transmission constraints, quantization can incorporate signal processing such as statistical classification, estimation, and modeling. We here survey several approaches to incorporating such tasks into the quantization and possible extensions to distributed signal processing.
37 citations
10 Sep 2000
TL;DR: The main contributions lie in the proposal of a coding strategy based on the magnitude of a DCT coefficient, the use of turbo codes for effective error correction, and the incorporation of JPEG quantization tables at embedding.
Abstract: We describe effective channel coding strategies which can be used in conjunction with linear programming optimization techniques for the embedding of robust perceptually adaptive DCT domain watermarks. The main contributions lie in the proposal of a coding strategy based on the magnitude of a DCT coefficient, the use of turbo codes for effective error correction, and finally the incorporation of JPEG quantization tables at embedding.
37 citations
TL;DR: It is shown that, by proper reorganization of its coefficients, the block-based DCT can have similar characteristics, such as energy compaction, cross-subband similarity, decay of magnitude across subband, etc., to the wavelet transform, and MRDCT is among the state-of-the-art DCT-based image coders reported in the literature.
Abstract: Recent success in discrete cosine transform (DCT) image coding is mainly attributed to recognition of the importance of data organization and representation. Currently, there are several competitive DCT-based coders such as DCT-based embedded image coding (EZDCT) (see Xiong et al., Z., 1996) and significance tree quantization (STQ) (see Davis, G. and Chawla, S., 1997). In the wavelet context, morphological representation of wavelet data has achieved the best compression performance. The representatives are morphological representation of wavelet data (MRWD) (see Servetto, S. et al., 1999) and significance-linked connected component analysis (see Chai, B.-B. et al., 1999). We show that, by proper reorganization of its coefficients, the block-based DCT can have similar characteristics, such as energy compaction, cross-subband similarity, decay of magnitude across subband, etc., to the wavelet transform. These characteristics can widen DCT applications relevant to image compression, image retrieval, pattern recognition, etc. We then present an image coder utilizing these characteristics by morphological representation of DCT coefficients (MRDCT). The experiments show that MRDCT is among the state-of-the-art DCT-based image coders reported in the literature. For example, for the Lena image at 0.25 bpp, MRDCT outperforms JPEG, STQ and EZDCT in peak signal-to-noise ratio by 1.0, 1.0, and 0.3 dB, respectively.
37 citations
Patent•
IBM1
TL;DR: In this paper, a method, apparatus and computer program product are provided for querying by image colors using a JPEG format for images from the internet, where a DCT coefficient is acquired from a JPEG image and compared with the selected image color.
Abstract: A method, apparatus and computer program product are provided for querying by image colors using a JPEG format for images from the internet. First an image color is selected. A DCT coefficient is acquired from a JPEG image. The acquired DCT coefficient is compared with the selected image color. A match within the JPEG image is identified responsive to the acquired DCT coefficient being near the selected image color. An average color can be used for querying JPEG formatted images from the internet.
37 citations
27 Apr 1995
TL;DR: This work presents a straightforward comparison between a wavelet decomposition and the well-known discrete cosine transform decomposition (as used in the JPEG compression standard), using comparable quantization and encoding strategies to isolate fundamental differences between the two methods.
Abstract: Wavelet-based image compression is receiving significant attention, largely because of its potential for good image quality at low bit rates. In medical applications, low bit rate coding may not be the primary concern, and it is not obvious that wavelet techniques are significantly superior to more established techniques at higher quality levels. In this work we present a straightforward comparison between a wavelet decomposition and the well-known discrete cosine transform decomposition (as used in the JPEG compression standard), using comparable quantization and encoding strategies to isolate fundamental differences between the two methods. Our focus is on the compression of single-frame, monochrome images taken from several common modalities (chest and bone x-rays and mammograms).© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
37 citations