38 research outputs found
A POCS-based restoration algorithm for restoring halftoned color-quantized images
Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe
Taming Reversible Halftoning via Predictive Luminance
Traditional halftoning usually drops colors when dithering images with binary
dots, which makes it difficult to recover the original color information. We
proposed a novel halftoning technique that converts a color image into a binary
halftone with full restorability to its original version. Our novel base
halftoning technique consists of two convolutional neural networks (CNNs) to
produce the reversible halftone patterns, and a noise incentive block (NIB) to
mitigate the flatness degradation issue of CNNs. Furthermore, to tackle the
conflicts between the blue-noise quality and restoration accuracy in our novel
base method, we proposed a predictor-embedded approach to offload predictable
information from the network, which in our case is the luminance information
resembling from the halftone pattern. Such an approach allows the network to
gain more flexibility to produce halftones with better blue-noise quality
without compromising the restoration quality. Detailed studies on the
multiple-stage training method and loss weightings have been conducted. We have
compared our predictor-embedded method and our novel method regarding spectrum
analysis on halftone, halftone accuracy, restoration accuracy, and the data
embedding studies. Our entropy evaluation evidences our halftone contains less
encoding information than our novel base method. The experiments show our
predictor-embedded method gains more flexibility to improve the blue-noise
quality of halftones and maintains a comparable restoration quality with a
higher tolerance for disturbances.Comment: to be published in IEEE Transactions on Visualization and Computer
Graphic
Media processor implementations of image rendering algorithms
Demands for fast execution of image processing are a driving force for today\u27s computing market. Many image processing applications require intense numeric calculations to be done on large sets of data with minimal overhead time. To meet this challenge, several approaches have been used. Custom-designed hardware devices are very fast implementations used in many systems today. However, these devices are very expensive and inflexible. General purpose computers with enhanced multimedia instructions offer much greater flexibility but process data at a much slower rate than the custom-hardware devices. Digital signal processors (DSP\u27s) and media processors, such as the MAP-CA created by Equator Technologies, Inc., may be an efficient alternative that provides a low-cost combination of speed and flexibility. Today, DSP\u27s and media processors are commonly used in image and video encoding and decoding, including JPEG and MPEG processing techniques. Little work has been done to determine how well these processors can perform other image process ing techniques, specifically image rendering for printing. This project explores various image rendering algorithms and the performance achieved by running them on a me dia processor to determine if this type of processor is a viable competitor in the image rendering domain. Performance measurements obtained when implementing rendering algorithms on the MAP-CA show that a 4.1 speedup can be achieved with neighborhood-type processes, while point-type processes achieve an average speedup of 21.7 as compared to general purpose processor implementations
Improved methods and system for watermarking halftone images
Watermarking is becoming increasingly important for content control and authentication. Watermarking seamlessly embeds data in media that provide additional information about that media. Unfortunately, watermarking schemes that have been developed for continuous tone images cannot be directly applied to halftone images. Many of the existing watermarking methods require characteristics that are implicit in continuous tone images, but are absent from halftone images. With this in mind, it seems reasonable to develop watermarking techniques specific to halftones that are equipped to work in the binary image domain. In this thesis, existing techniques for halftone watermarking are reviewed and improvements are developed to increase performance and overcome their limitations. Post-halftone watermarking methods work on existing halftones. Data Hiding Cell Parity (DHCP) embeds data in the parity domain instead of individual pixels. Data Hiding Mask Toggling (DHMT) works by encoding two bits in the 2x2 neighborhood of a pseudorandom location. Dispersed Pseudorandom Generator (DPRG), on the other hand, is a preprocessing step that takes place before image halftoning. DPRG disperses the watermark embedding locations to achieve better visual results. Using the Modified Peak Signal-to-Noise Ratio (MPSNR) metric, the proposed techniques outperform existing methods by up to 5-20%, depending on the image type and method considered. Field programmable gate arrays (FPGAs) are ideal for solutions that require the flexibility of software, while retaining the performance of hardware. Using VHDL, an FPGA based halftone watermarking engine was designed and implemented for the Xilinx Virtex XCV300. This system was designed for watermarking pre-existing halftones and halftones obtained from grayscale images. This design utilizes 99% of the available FPGA resources and runs at 33 MHz. Such a design could be applied to a scanner or printer at the hardware level without adversely affecting performance
Low-complexity high-performance multiscale error diffusion technique for digital halftoning
Version of RecordPublishe
Efficient Halftoning via Deep Reinforcement Learning
Halftoning aims to reproduce a continuous-tone image with pixels whose
intensities are constrained to two discrete levels. This technique has been
deployed on every printer, and the majority of them adopt fast methods (e.g.,
ordered dithering, error diffusion) that fail to render structural details,
which determine halftone's quality. Other prior methods of pursuing visual
pleasure by searching for the optimal halftone solution, on the contrary,
suffer from their high computational cost. In this paper, we propose a fast and
structure-aware halftoning method via a data-driven approach. Specifically, we
formulate halftoning as a reinforcement learning problem, in which each binary
pixel's value is regarded as an action chosen by a virtual agent with a shared
fully convolutional neural network (CNN) policy. In the offline phase, an
effective gradient estimator is utilized to train the agents in producing
high-quality halftones in one action step. Then, halftones can be generated
online by one fast CNN inference. Besides, we propose a novel anisotropy
suppressing loss function, which brings the desirable blue-noise property.
Finally, we find that optimizing SSIM could result in holes in flat areas,
which can be avoided by weighting the metric with the contone's contrast map.
Experiments show that our framework can effectively train a light-weight CNN,
which is 15x faster than previous structure-aware methods, to generate
blue-noise halftones with satisfactory visual quality. We also present a
prototype of deep multitoning to demonstrate the extensibility of our method