We propose a multi-component metric for the evaluation of digital or video cameras under wide dynamic range (WDR)
scenes. The method is based on a single image capture using a specifically designed WDR test chart and light box. Test
patterns on the WDR test chart include gray ramps, color patches, arrays of gray patches, white bars, and a relatively
dark gray background. The WDR test chart is professionally made using 3 layers of transparencies to produce a contrast
ratio of approximately 110 dB for WDR testing. A light box is designed to provide a uniform surface with light level at
about 80K to 100K lux, which is typical of a sunny outdoor scene.
From a captured image, 9 image quality component scores are calculated. The components include number of resolvable
gray steps, dynamic range, linearity of tone response, grayness of gray ramp, number of distinguishable color patches,
smearing resistance, edge contrast, grid clarity, and weighted signal-to-noise ratio. A composite score is calculated from
the 9 component scores to reflect the comprehensive image quality in cameras under WDR scenes. Experimental results
have demonstrated that the multi-component metric corresponds very well to subjective evaluation of wide dynamic
range behavior of cameras.
In this paper, we examine a method for estimating the embedding rate (or equivalently, the length of the hidden message) for plus-minus one embedding. The plus-minus one method embeds information into the least significant bit of the carrier by either adding or subtracting one from a pixel when the LSB of the pixel value is not equal to the message bit. The decision of adding or subtracting one is determined randomly with equal probability for each case. In this paper, we formulate the plus-minus one embedding procedure as a state transition between two sequences of sets. Given an image that possibly contains plus-minus one embedding, an original image is estimated using an adaptive wavelet denoising algorithm. The stego image is classified into busy and non-busy areas, and then parameters with respect to the state transition are calculated from the non-busy areas in both the stego image and the estimated original image. The embedding rate is then estimated using a symmetric form of divergence (Kullback-Leibler distance). Experimental results using the proposed algorithm are shown.
The problem of embedding a binary image into another binary image is considered. We propose a method where the embedding is performed spatially, i.e., a smaller sized binary image (Image A) is embedded in a larger sized binary image (Image B). Image B is the only visible image when the composite is viewed directly. To view Image A, we can perform a suitable extraction operation on Image B. The embedding algorithm can be applied recursively so that the final binary image can contain a number of progressively smaller sized binary images. Examples are used to show the quality of the embedding. Admissible conditions and security issues of the embedding algorithm are considered.
In previous work, Wong had proposed both secret key and public key watermarking schemes for image authentication that can detect and localized any change made to a watermarked image. The techniques proposed were block-based, that is, they partitioned the image into non-overlapping blocks and separately authenticated each block. Subsequently, Holliman and Memon observed that many block based watermarking schemes are vulnerable to substitution attacks. They specifically showed that the Wong schemes can be attacked using a 'vector quantization' (VQ) approach. This attack exploits that fact that if a sufficient number of images containing the same watermark bitmap is available, then one can use a VQ-like technique to forge a watermark into a new image. About the same time and independently, Coppersmith et al. proposed to use overlapping blocks to resist this attack. Although this method can make the attack inefficient, it does so with a significant loss of the localization property of the watermark. We extend in this paper the Wong schemes so that the resulting algorithms can resist the VQ attack and at the same time provide the same localization property in the watermark as the original schemes. The key idea is to insert a unique image-dependent block ID into the watermarking process so that the VQ attack will not have a rich enough 'codebook' to forge the watermark.
Digital watermarks have recently been proposed for the purposes of copy protection and copy deterrence for multimedia content. In copy deterrence, a content owner (seller) inserts a unique watermark into a copy of the content before it is sold to a buyer. If the buyer resells unauthorized copies of the watermarked content, then these copies can be traced to the unlawful reseller (original buyer) using a watermark detection algorithm. One problem with such an approach is that the original buyer whose watermark has been found on unauthorized copies can claim that the unauthorized copy was created or caused (for example, by a security breach) by the original seller. In this paper we propose an interactive buyer-seller protocol for invisible watermarking in which the seller does not get to know the exact watermarked copy that the buyer receives. Hence the seller cannot create copies of the original content containing the buyer's watermark. In cases where the seller finds an unauthorized copy, the seller can identify the buyer from a watermark in the unauthorized copy, and furthermore the seller can prove this fact to a third party using a dispute resolution protocol. This prevents the buyer from claiming that an unauthorized copy may have originated from the seller.
The quality of typical error diffused images can be improved by designing an error diffusion filter that minimizes a frequency weighted mean squared error between the continuous tone input and the halftone output. Previous approaches to this design are typically based on an assumption that the binary quantizer error is a white noise source. We propose in this paper an iterative method for designing an optimum error diffusion kernel without such an assumption on the spectral characteristics of the binary quantizer error.In particular, we use a set of training images, and iterate the two steps of designing the error diffusion filter and evaluating the spectrum of the quantizer error. Experimental results are shown for error diffusion filters designed using this iterative method.
We describe a new halftoning algorithm that uses a multiscale dot distribution procedure over a rotated quad-tree structure. The algorithm ensures that the average graylevel of the grayscale image over any node of the rotated quad-tree is equal to that of the halftone at the same node. This is achieved by first deciding the total number of black and white dots to be placed over the entire halftone, and then distributing the dots recursively to the subregions represented by the nodes of the rotated quad-tree.
A new halftoning algorithm is proposed that incorporates the concept of delayed decision in error diffusion. This algorithm is similar to a recently proposed tree coding halftoner in that both examine at each pixel location a neighborhood of future (with respect to the scanning direction) pixels before deciding the value of the current output pixel. Because of the feedback in the error diffusion process, one can reduce the size of the 'look ahead' window compared to a typical tree-coding halftoner, resulting in a relatively low complexity. It is shown that this algorithm improves the quality of halftone images compared to those generated by traditional error diffusion.
We describe a procedure by which JPEG compression may be customized for grayscale images that are to be compressed before they are scaled, halftoned, and printed. Our technique maintains 100% compatibility with the JPEG standard, and is applicable with all scaling and halftoning methods. The JPEG quantization table is designed using frequency-domain characteristics of the scaling and halftoning operations, as well as the frequency sensitivity of the human visual system. In addition, the Huffman tables are optimized for low-rate coding. Compression artifacts are greatly reduced because they are masked by the halftoning patterns, and pushed into frequency bands where the eye is less sensitive. We present experimental results demonstrating that the customized JPEG encoder typically maintains `near visually lossless' image quality at rates below 0.2 bits per pixel (with reference to the final, printed image). In terms of the achieved bit rate, this performance is typically at least 20% better than that of a JPEG encoder using the suggested baseline tables.
Error diffusion is a procedure for generating high quality bi-level images from continuous tone images so that both the continuous and halftone images appear similarly when observed from a distance. It is well known that certain objectionable patterning artifacts can occur in error diffused images. Previous approaches for improving the quality of error diffused images include the application of non-standard scanning strategies (e.g., serpentine or Peano scanning), dithering the filter coefficients in error diffusion, dithering the quantizer threshold, incorporating feedback to control the average distance between dots, and designing an optimum error diffusion filter according to some distortion criterion. Here we consider a method for adjusting the error diffusion filter concurrently with the error diffusion process so that an error criterion is minimized. The minimization is performed using the LMS algorithm in adaptive signal processing. Such an algorithm produces better halftone image quality compared to traditional error diffusion with a fixed filter.
We consider an image coder that uses a multiscale decomposition. An image is first whitened, and then decomposed into subimages using a wavelet decomposition. A bit allocation algorithm is employed that assigns various rates to the subimages according to the power spectral density of the original image. Based on the bit assignments, scalar quantizers are used for encoding the coefficients. To improve the performance of this coder, we consider a bit allocation procedure that takes the response of the human visual system into account. Finally, we introduce a spatial partition on top of the multiscale decomposition, resulting in a substantial improvement to the image quality.
Chain codes are widely nsed for representing a line drawing digitally using connected short line segments. When the encoded image is presented in the usual way, the resolution is constrained by the grid size. Here we consider using a linear smoothing filter for improving the reconstruction accuracy. The linear reconstruction filter that minimizes the meansquare reconstruction error is derived, and it is found that subpixel accuracy can be achieved by using such a filter.
This course will provide an introduction to the fundamental concepts of digital halftoning. Overview of the most successful digital halftoning algorithms will be given. We will discuss basic aspects of printer modeling and color as they pertain to digital halftoning, compression, watermarking, and descreening of digital halftones.
This course provides an introduction to the fundamental concepts of digital halftoning with an overview of the most successful digital halftoning algorithms. We discuss basic aspects of printer modeling and color as they pertain to digital halftoning, compression, watermarking, and descreening of digital halftones.
This course provides an in-depth look at advanced concepts of digital halftoning. Monochrome and color models for the rendering device and human visual system are presented, and model-based, iterative approaches to digital halftoning and application to model-based monochrome and color halftoning are discussed.