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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TL;DR: A novel for-mulation of the state and state-transition rule that uses a perceptually based edge classifier is introduced and significant gains to be obtained are obtained by enhancing the basic VQ approach with interblock memory.
Abstract: Image compression using memoryless vector quantization (VQ), in which small blocks (vectors) of pixels are independently encoded, has been demonstrated to be an effective technique for achieving bit rates above 0.6 bits per pixel (bpp). To maintain the same quality at lower rates, it is necessary to exploit spatial redundancy over a larger region of pixels than is possible with memoryless VQ. This can be achieved by incorporating memory of previously encoded blocks into the encoding of each successive input block. Finite-state vector quantization (FSVQ) employs a finite number of states, which summarize key information about previously encoded vectors, to select one of a family of codebooks to encode each input vector. In this paper, we review the basic ideas of VQ and extend the finite-state concept to image compression. We introduce a novel for-mulation of the state and state-transition rule that uses a perceptually based edge classifier. We also examine the use of interpolation in conjunction with VQ with finite memory. Coding results are presented for monochrome images in the bit-rate range of 0.24 to 0.32 bpp. The results achieved with finite memory are comparable to those of memoryless VQ at 0.6 bpp and show that there are significant gains to be obtained by enhancing the basic VQ approach with interblock memory.
108 citations
TL;DR: This paper presents a content-based digital image-watermarking scheme, which is robust against a variety of common image-processing attacks and geometric distortions and yields a better performance as compared with some peer systems in the literature.
107 citations
TL;DR: Experiments with visually impaired patients show improved perceived image quality at moderate levels of enhancement but rejection of artifacts caused by higher levels of Enhancement, suggesting a need for further research into this area.
Abstract: An image enhancement algorithm for low-vision patients was developed for images compressed using the JPEG standard. The proposed algorithm enhances the images in the discrete cosine transform domain by weighting the quantization table in the decoder. Our specific implementation increases the contrast at all bands of frequencies by an equal factor. The enhancement algorithm has four advantages: 1) low computational cost; 2) suitability for real-time application; 3) ease of adjustment by end-users (for example, adjusting a single parameter); and 4) less severe block artifacts as compared with conventional (post compression) enhancements. Experiments with visually impaired patients show improved perceived image quality at moderate levels of enhancement but rejection of artifacts caused by higher levels of enhancement.
107 citations
TL;DR: It is indicated that there exists strong correlation between the optimal mean squared error threshold and the image quality factor Q, which is selected in the encoding end and can be computed from the quantization table embedded in the JPEG file.
Abstract: We propose a simple yet effective deblocking method for JPEG compressed image through postfiltering in shifted windows (PSW) of image blocks. The MSE is compared between the original image block and the image blocks in shifted windows, so as to decide whether these altered blocks are used in the smoothing procedure. Our research indicates that there exists strong correlation between the optimal mean squared error threshold and the image quality factor Q, which is selected in the encoding end and can be computed from the quantization table embedded in the JPEG file. Also we use the standard deviation of each original block to adjust the threshold locally so as to avoid the over-smoothing of image details. With various image and bit-rate conditions, the processed image exhibits both great visual effect improvement and significant peak signal-to-noise ratio gain with fairly low computational complexity. Extensive experiments and comparison with other deblocking methods are conducted to justify the effectiveness of the proposed PSW method in both objective and subjective measures.
107 citations
TL;DR: The problem of evaluating the uncertainty that characterises discrete Fourier transform output data is dealt with, using a method based on a ‘white box’ theoretical approach, which can be particularly useful for any designer and user of DFT-based instruments.
107 citations