Color error-diffusion halftoning

Grayscale halftoning converts a continuous-tone image (e.g., 8 bits per pixel) to a lower resolution (e.g., 1 bit per pixel) for printing or display. Grayscale halftoning by error diffusion uses feedback to shape the quantization noise into high frequencies where the human visual system (HVS) is least sensitive. In color halftoning, the application of grayscale error-diffusion methods to the individual colorant planes fails to exploit the HVS response to color noise. Ideally the quantization error must be diffused to frequencies and colors, to which the HVS is least sensitive. Further it is desirable for the color quantization to take place in a perceptual space so that the colorant vector selected as the output color is perceptually closest to the color vector being quantized. This article discusses the design principles of color error diffusion that differentiate it from grayscale error diffusion, focusing on color error diffusion halftoning systems using the red, green, and blue (RGB) space for convenience.     

A POCS-based restoration algorithm for restoring halftoned color-quantized images.

This paper studies the restoration of images which are color-quantized with error diffusion. Though there are many reported algorithms proposed for restoring noisy blurred color images and inverse halftoning, restoration of color-quantized images is rarely addressed in the literature especially when the images are color-quantized with halftoning. Direct application of existing restoration techniques are generally inadequate to deal with this problem. In this paper, a restoration algorithm based on projection onto convex sets is proposed. This algorithm makes use of the available color palette and the mechanism of a halftoning process to derive useful a priori information for restoration. Simulation results showed that it could improve the quality of a halftoned color-quantized image remarkably in terms of both SNR and CIELAB color difference metric.     

Look-up table (LUT) method for inverse halftoning

In this paper we propose look-up table (LUT) based methods for inverse halftoning of images. The LUT for inverse halftoning is obtained from the histogram gathered from a few sample halftone images and corresponding original images. The method is extremely fast (no filtering is required) and the PSNR and visual image quality achieved is comparable to the best methods known for inverse halftoning. The LUT inverse halftoning method does not depend on the specific properties of the halftoning method, and can be applied to any halftoning method. Then, an algorithm for template selection for LUT inverse halftoning is introduced. We demonstrate the performance of the LUT inverse halftoning algorithm on error diffused images and ordered dithered images. We also extend LUT inverse halftoning to color halftones.     

Blue and green noise halftoning models

In this article, we review the spatial and spectral characteristics of blue- and green-noise halftoning models. In the case of blue noise, dispersed-dot dither patterns are constructed by isolating minority pixels as homogeneously as possible, and by doing so, a pattern composed exclusively of high-frequency spectral components is produced. Blue-noise halftoning is preferred for display devices that can accommodate isolated dots such as various video displays and some print technologies such as ink-jet. For print marking engines that cannot support isolated pixels, dispersed-dot halftoning is inappropriate. For such cases, clustered-dot halftoning is used to avoid dot-gain instability. Green-noise halftones are clustered-dot, blue-noise patterns. Such patterns enjoy the blue-noise properties of homogeneity and lack low-frequency texture but have clusters of minority pixels on blue-noise centers. Green noise is composed exclusively of midfrequency spectral components. In addition to the basic spatial and spectral characteristics of the halftoning models, this article also reviews some of the earlier work done to improve error diffusion as a noise generator. We also discuss processes to generate threshold arrays to achieve blue and green noise with the computationally efficient process of ordered dither.     

Compressed domain video saliency detection using global and local spatiotemporal features

A compressed domain video saliency detection algorithm, which employs global and local spatiotemporal (GLST) features, is proposed in this work. We first conduct partial decoding of a compressed video bitstream to obtain motion vectors and DCT coefficients, from which GLST features are extracted. More specifically, we extract the spatial features of rarity, compactness, and center prior from DC coefficients by investigating the global color distribution in a frame. We also extract the spatial feature of texture contrast from AC coefficients to identify regions, whose local textures are distinct from those of neighboring regions. Moreover, we use the temporal features of motion intensity and motion contrast to detect visually important motions. Then, we generate spatial and temporal saliency maps, respectively, by linearly combining the spatial features and the temporal features. Finally, we fuse the two saliency maps into a spatiotemporal saliency map adaptively by comparing the robustness of the spatial features with that of the temporal features. Experimental results demonstrate that the proposed algorithm provides excellent saliency detection performance, while requiring low complexity and thus performing the detection in real-time.     

Printer models and error diffusion

A new model-based approach to digital halftoning is proposed. It is intended primarily for laser printers, which generate "distortions" such as "dot overlap". Conventional methods, such as clustered-dot ordered dither, resist distortions at the expense of spatial and gray-scale resolution. The proposed approach relies on printer models that predict distortions, and rather than merely resisting them, it exploits them to increase, rather than decrease, both spatial and gray-scale resolution. We propose a general framework for printer models and find a specific model for laser printers. As an example of model-based halftoning, we propose a modification of error diffusion, which is often considered the best halftoning method for CRT displays with no significant distortions. The new version exploits the printer model to extend the benefits of error diffusion to printers. Experiments show that it provides high-quality reproductions with reasonable complexity. The proposed modified error diffusion technique is compared with Stucki's (1981) MECCA, which is a similar but not widely known technique that accounts for dot overlap. Model-based halftoning can be especially useful in transmission of high-quality documents using high-fidelity gray-scale image encoders.     

Hardcopy image barcodes via block-error diffusion.

Error diffusion halftoning is a popular method of producing frequency modulated (FM) halftones for printing and display. FM halftoning fixes the dot size (e.g., to one pixel in conventional error diffusion) and varies the dot frequency according to the intensity of the original grayscale image. We generalize error diffusion to produce FM halftones with user-controlled dot size and shape by using block quantization and block filtering. As a key application, we show how block-error diffusion may be applied to embed information in hardcopy using dot shape modulation. We enable the encoding and subsequent decoding of information embedded in the hardcopy version of continuous-tone base images. The encoding-decoding process is modeled by robust data transmission through a noisy print-scan channel that is explicitly modeled. We refer to the encoded printed version as an image barcode due to its high information capacity that differentiates it from common hardcopy watermarks. The encoding/halftoning strategy is based on a modified version of block-error diffusion. Encoder stability, image quality versus information capacity tradeoffs, and decoding issues with and without explicit knowledge of the base image are discussed.     

场序彩色视频控制系统

采用时序彩色法实现彩色显示,在液晶显示技术中具有良好的发展前景,具有成本低、分辨率高、彩色效果好等优点。文章主要介绍了一套针对南开大学研制的LCoS芯片的场序彩色视频控制系统,该系统采用场序彩色模式,经过实验调试后,最终实现了LCoS芯片的彩色视频显示。在设计的系统中视频数据采用并行的输出方式,降低了整个控制系统的工作频率。系统利用两组外部SRAM对帧数据进行缓存,采用乒乓操作来实现视频的实时连续显示。整个控制系统的核心部分采用一片FPGA来实现,文章详细介绍了FPGA内部数据流处理的算法实现:FPGA采用3组移位寄存器对数据进行动态缓存,实现了串行-并行的数据转换,并将同时输入的红、绿、蓝视频数据转换为红、绿、蓝子场数据;并且充分利用FP-GA内部RAM作为缓存,完成了图像的插值处理,以满足LCoS扫描显示的要求。最后介绍了在时序彩色LCoS显示中出现的问题和难点。     

基于FPGA的可变规模多格式YCbCr到RGB快速转换模块设计

文章介绍了YCbCr色彩空间和RGB色彩空间之间的转换的方法,实现了不同规模以及不同数据结构的YCbCr到RGB的快速硬件转换。采用数据重排列和数据分离等方法,不仅支持QCIF到HDTV多种分辨率的视频转换,而且支持YCbCr444、YCbCr422和YCbCr420等多种打包或平面YCbCr格式。本设计方案已用VerilogHDL语言实现,并在FPGA平台验证通过,转换后的RGB视频可直接输出到显示器显示,并能实现视频控制,存储,回放等功能。     

A technique for producing scalable color-quantized images with error diffusion.

To reliably and efficiently deliver media information to diverse clients over heterogeneous networks, the media involved must be scalable. In this paper, a color quantization algorithm for generating scalable color-indexed images is proposed based on a multiscale error diffusion framework. Images of lower resolutions are embedded in the outputs such that a simple down-sampling process can extract images of any desirable resolutions. Images possessing this scalable property support transmission over the Internet which contains clients with different display resolutions, systems with different caching resources and networks with varying bandwidths and QoS capabilities. Unlike most of the color halftoning algorithms available nowadays, the proposed algorithm is not dedicated for printing applications but for color-indexed displays. It works with any arbitrary palettes of different size.     

Digital color halftoning with generalized error diffusion andmultichannel green-noise masks

In this paper, we introduce two novel techniques for digital color halftoning with green-noise-stochastic dither patterns generated by homogeneously distributing minority pixel clusters. The first technique employs error diffusion with output-dependent feedback where, unlike monochrome image halftoning, an interference term is added such that the overlapping of pixels of different colors can be regulated for increased color control. The second technique uses a green-noise mask, a dither array designed to create green-noise halftone patterns, which has been constructed to also regulate the overlapping of different colored pixels. As is the case with monochrome image halftoning, both techniques are tunable, allowing for large clusters in printers with high dot-gain characteristics, and small clusters in printers with low dot-gain characteristics.     

A new optimal digital halftoning technique based on the discrete cosine transform

Digital halftoning is a technique to display a gray-level image with a bilevel device. Conventionally, most halftoning techniques are done in the spatial domain. A new halftoning technique based on the discrete cosine transform is proposed. The method chooses an optimal bilevel image to display the original gray-level image and minimize the weighted mean square error based on the discrete cosine transform domain. The simulation results indicate that our algorithm can produce very good halftoned images without false contours.     

Model-based digital halftoning

Digital halftoning is the process of generating a pattern of pixels with a limited number of colors that, when seen by the human eye, is perceived as a continuous-tone image. Digital halftoning is used to display continuous-tone images in media in which the direct rendition of the tones is impossible. The most common example of such media is ink or toner on paper, and the most common rendering devices for such media are, of course, printers. Halftoning works because the eye acts as a spatial low-pass filter that blurs the rendered pixel pattern, so that it is perceived as a continuous-tone image. Although all halftoning methods rely at least implicitly, on some understanding of the properties of human vision and the display device, the goal of model-based halftoning techniques is to exploit explicit models of the display device and the human visual system (HVS) to maximize the quality of the displayed images. Based on the type of computation involved, halftoning algorithms can be broadly classified into three categories: point algorithms (screening or dithering), neighborhood algorithms (error diffusion), and iterative algorithms [least squares and direct binary search (DBS)]. All of these algorithms can incorporate HVS and printer models. The best halftone reproductions, however, are obtained by iterative techniques that minimize the (squared) error between the output of the cascade of the printer and visual models in response to the halftone image and the output of the visual model in response to the original continuous-tone image.     

Digital color halftoning

Digital color halftoning is the process of transforming continuous-tone color images into images with a limited number of colors. The importance of this process arises from the fact that many color imaging systems use output devices such as color printers and low-bit depth displays that are bilevel or multilevel with a few levels. The goal is to create the perception of a continuous-tone color image using the limited spatiochromatic discrimination capability of the human visual system. In decreasing order of how locally algorithms transform a given image into a halftone and, therefore, in increasing order of computational complexity and halftone quality, monochrome digital halftoning algorithms can be placed in one of three categories: 1) point processes (screening or dithering), 2) neighborhood algorithms (error diffusion), and 3) iterative methods. All three of these algorithm classes can be generalized to digital color halftoning with some modifications. For an in-depth discussion of monochrome halftoning algorithms, the reader is directed to the July 2003 issue of IEEE Signal Processing Magazine. In the remainder of this article, we only address those aspects of halftoning that specifically have to do with color. For a good overview of digital color halftoning, the reader is directed to Haines et al. (2003). In addition, Agar et al. (2003) contains a more in-depth treatment of some of the material found in this work.     

Spatiotemporal saliency detection and salient region determination for H.264 videos

In this study, a spatiotemporal saliency detection and salient region determination approach for H.264 videos is proposed. After Gaussian filtering in Lab color space, the phase spectrum of Fourier transform is used to generate the spatial saliency map of each video frame. On the other hand, the motion vector fields from each H.264 compressed video bitstream are backward accumulated. After normalization and global motion compensation, the phase spectrum of Fourier transform for the moving parts is used to generate the temporal saliency map of each video frame. Then, the spatial and temporal saliency maps of each video frame are combined to obtain its spatiotemporal saliency map using adaptive fusion. Finally, a modified salient region determination scheme is used to determine salient regions (SRs) of each video frame. Based on the experimental results obtained in this study, the performance of the proposed approach is better than those of two comparison approaches.     

Spatiotemporal segmentation for compact video representation

In this paper, a novel hierarchical object-oriented video segmentation and representation algorithm is proposed. The local variance contrast and the frame difference contrast are jointly exploited for structural spatiotemporal video segmentation because these two visual features can indicate the spatial homogeneity of the grey levels and the temporal coherence of the motion fields efficiently, where the two-dimensional (2D) spatiotemporal entropic technique is further selected for generating the 2D thresholding vectors adaptively according to the variations of the video components. After the region growing and edge simplification procedures, the accurate boundaries among the different video components are further exploited by an intra-block edge extraction procedure. Moreover, the relationships of the video components among frames are exploited by a temporal tracking procedure. This proposed object-oriented spatiotemporal video segmentation algorithm may be useful for MPEG-4 system generating the video object plane (VOP) automatically.     

Adaptive methods for dithering color images

Most color image printing and display devices do not have the capability of reproducing true color images. A common remedy is the use of dithering techniques that take advantage of the lower sensitivity of the eye to spatial resolution and exchange higher color resolution with lower spatial resolution. An adaptive error diffusion method for color images is presented. The error diffusion filter coefficients are updated by a normalized least mean square-type (LMS-type) algorithm to prevent textural contours, color impulses, and color shifts, which are among the most common side effects of the standard dithering algorithms. Another novelty of the new method is its vector character: previous applications of error diffusion have treated the individual color components of an image separately. We develop a general vector approach and demonstrate through simulation studies that superior results are achieved.     

Model-based color halftoning using direct binary search.

In this paper, we develop a model-based color halftoning method using the direct binary search (DBS) algorithm. Our method strives to minimize the perceived error between the continuous tone original color image and the color halftone image. We exploit the differences in how the human viewers respond to luminance and chrominance information and use the total squared error in a luminance/chrominance based space as our metric. Starting with an initial halftone, we minimize this error metric using the DBS algorithm. Our method also incorporates a measurement based color printer dot interaction model to prevent the artifacts due to dot overlap and to improve color texture quality. We calibrate our halftoning algorithm to ensure accurate colorant distributions in resulting halftones. We present the color halftones which demonstrate the efficacy of our method.     

高分辨率光谱视频采集研究

相比于当前常用的红、绿、蓝三通道成像技术,光谱采集能够提供更多的颜色通道,从而更加精细地反映光源与场景的物理特性,因此可以广泛应用于遥感、材料、医疗、环境等诸多领域.光谱采集技术需要捕获光线在不同时刻和不同空间位置的光谱信息,主要表现在光谱、时间、空间三个维度上的信息获取.传统的光谱采集设备大多采用时序滤波或空间扫描机制记录光谱信息,需要很长的成像时间,因而不能用于光谱视频信息的采集.近年来,伴随着采样理论和电子元器件技术的迅速发展,光谱的视频(动态)采集成为了可能.本文着眼于近年来刚刚起步的光谱视频采集技术,从采集技术的背景和基本原理出发,对于近年来涌现的高分辨率光谱视频采集方法和系统进行介绍,并对于基于直接采集和计算重构的各类技术手段展开讨论,详细阐述这些方法的光学原理、采集理论以及实际系统构建中的问题.此外,本文对各种方法在光谱视频采集中的一些重要性能指标和系统实际运行优缺点进行对比.最后,本文也对光谱视频采集的相关应用与发展前景进行了讨论.     

基于ARM处理器S3C2440的VGA显示技术

基于VGA接口时序以,高性能视频D/A芯片ADV7120为核心,实现了基于嵌入式CPU S3C2440的VGA显示子系统。系统一方面利用S3C2440自带的LCD控制器产生符合VGA显示要求的时序逻辑,另一方面通过LCD数据线将数字RGB信号传递给具有8路通道的视频D/A芯片ADV7120,产生VGA显示需要的模拟色彩信号。通过TFTLCD扫描显示的时序与VGA扫描显示时序的匹配,驱动VGA显示屏。该系统能够达到正常显示色彩信息的要求,且价格低廉,适用于对显示效果要求不苛刻,但要求大尺寸显示屏且对价格敏感的嵌入式应用中。