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.
Papers published on a yearly basis
Papers
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20 Sep 2005
TL;DR: Image pre-filtering is shown to be expedient for coded image quality improvement and/or increase of compression ratio and some recommendations on how to set the compression ratio to provide quasioptimal quality of coded images are given.
Abstract: Lossy compression of noise-free and noisy images differs from each other. While in the first case image quality is decreasing with an increase of compression ratio, in the second case coding image quality evaluated with respect to a noise-free image can be improved for some range of compression ratios. This paper is devoted to the problem of lossy compression of noisy images that can take place, e.g., in compression of remote sensing data. The efficiency of several approaches to this problem is studied. Image pre-filtering is shown to be expedient for coded image quality improvement and/or increase of compression ratio. Some recommendations on how to set the compression ratio to provide quasioptimal quality of coded images are given. A novel DCT-based image compression method is briefly described and its performance is compared to JPEG and JPEG2000 with application to lossy noisy image coding.
40 citations
TL;DR: It is demonstrated that the proposed algorithm is significantly more efficient than the conventional filtered spatial domain and earlier proposed DCT domain methods.
Abstract: A method for efficient spatial domain filtering, directly in the discrete cosine transform (DCT) domain, is developed and proposed. It consists of using the discrete sine transform (DST) and the DCT for transform-domain processing on the in JPEG basis of the previously derived convolution-multiplication properties of discrete trigonometric transforms. The proposed scheme requires neither zero padding of the input data nor kernel symmetry. It is demonstrated that, in typical applications, the proposed algorithm is significantly more efficient than the conventional filtered spatial domain and earlier proposed DCT domain methods. The proposed method is applicable to any DCT-based image compression standard, such as JPEG, MPEG, and H.261.
40 citations
TL;DR: An effective method to detect the recompression in the color images by using the conversion error, rounding error, and truncation error on the pixel in the spherical coordinate system is proposed and experimental results show that the performance of the proposed method is better than the existing methods.
Abstract: Detection of double Joint Photographic Experts Group (JPEG) compression is an important part of image forensics. Although methods in the past studies have been presented for detecting the double JPEG compression with a different quantization matrix, the detection of double JPEG compression with the same quantization matrix is still a challenging problem. In this paper, an effective method to detect the recompression in the color images by using the conversion error, rounding error, and truncation error on the pixel in the spherical coordinate system is proposed. The randomness of truncation errors, rounding errors, and quantization errors result in random conversion errors. The pixel number of the conversion error is used to extract six-dimensional features. Truncation error and rounding error on the pixel in its three channels are mapped to the spherical coordinate system based on the relation of a color image to the pixel values in the three channels. The former is converted into amplitude and angles to extract 30-dimensional features and 8-dimensional auxiliary features are extracted from the number of special points and special blocks. As a result, a total of 44-dimensional features have been used in the classification by using the support vector machine (SVM) method. Thereafter, the support vector machine recursive feature elimination (SVMRFE) method is used to improve the classification accuracy. The experimental results show that the performance of the proposed method is better than the existing methods.
40 citations
01 May 1994
TL;DR: A decompression algorithm with reduction in both ringing and blocking artifacts is described, which is particularly dominant in document images, where sharp edges present commonly in text and graphics are more likely to be encountered.
Abstract: The major artifacts of JPEG compressed images are blocking and ringing, which are mainly due to the quantization of low frequency and high frequency DCT components respectively. The ringing artifacts are particularly dominant in document images, where sharp edges present commonly in text and graphics are more likely to be encountered. In this paper we describe a decompression algorithm with reduction in both ringing and blocking artifacts.
40 citations
TL;DR: A blockwise distortion measure is proposed for evaluating the visual quality of compressed images, which outperforms PQS for a set of test images, and is much simpler to implement.
Abstract: A blockwise distortion measure is proposed for evaluating the visual quality of compressed images. The proposed measure calculates quantitatively how well important visual properties have been preserved in the distorted image. The method consists of three quality factors detecting contrast errors, structural errors, and quantization errors. The proposed method outperforms PQS for a set of test images, and is much simpler to implement. The method should also be applicable to color images; properties like color richness and saturation are captured by the quantization and contrast measures, respectively.
40 citations