Guotong Feng

High-Quality MRC Document Coding

The mixed raster content (MRC) model can be used to implement highly effective document compression algorithms. MRC document coders are typically based on the use of a binary mask layer that efficiently encodes the text and graphic content. However, while many MRC-based methods can yield much higher compression ratios than conventional color image compression methods, the binary representation tends to distort fine document details, such as thin lines and text edges. In this paper, we propose a method for encoding and decoding the binary mask layer that substantially improves the decoded document fidelity of text and graphics at a fixed bit rate. This method, which we call resolution-enhanced rendering (RER), works by adaptively dithering the encoded binary mask, and then applying a nonlinear predictor to decode a gray level mask at the same resolution. Both the dithering and nonlinear prediction algorithms are jointly optimized to produce the minimal distortion rendering. In addition, we introduce a second method, interpolative RER (IRER), which incorporates interpolation into the MRC decoder. The IRER method increases the compression ratio by allowing a high-resolution document to be coded at lower resolutions. We present experimental results illustrating the performance of our RER/IRER methods and comparing them to some existing MRC-based compression algorithms

Spherical coded imagers: improving lens speed, depth-of-field, and manufacturing yield through enhanced spherical aberration and compensating image processing

Recently, joint analysis and optimization of both the optical sub-system and the algorithmic capabilities of digital processing have created new digital-optical imaging systems with system-level benefits. We explore a special class of digital-optical imaging systems called spherical coding that combine lens systems having controlled amounts of spherical aberration with digital sharpening filters to achieve fast, low-cost, extended depth-of-field (EDoF) imaging systems. We provide analysis of the optimal amount of spherical aberration required as a function of desired depth-of-field extension. We also characterize the MSE-optimal filters required to restore contrast. Finally, we describe a simple method to designing spherical coded systems and demonstrate several advantages such as improved manufacturing yield using an actual lens design.

Rate-distortion-based segmentation for MRC compression

Effective document compression algorithms require scanned document images be first segmented into regions such as text, pictures and background. In this paper, we present a document compression algorithm that is based on the 3-layer (foreground/mask/background)MRC (mixture raster content) model. This compression algorithm first segments a scanned document image into different classes. Then, each class is transformed to the 3-layer MRC model differently according to the property of that class. Finally, the foreground and the back-ground layers are compressed using JPEG with customized quantization tables. The mask layer is compressed using JBIG2. The segmentation is optimized in the sense of rate distortion for the 3-layer MRC representation. It works in a closed loop fashion by a lying each transformation to each region of the document and then selecting the method that yields the best rate-distortion trade-off. The proposed segmentation algorithm can not only achieve a better rate-distortion trade-off, but also produce more robust segmentations by eliminating those mis-classifications which can cause severe artifacts. At similar bit rates, our MRC compression with the rate- distortion based segmentation can achieve a much higher subjective quality than state-of-the-art compression algorithms, such as JPEG and JPEG-2000.

Rate distortion optimized document coding using resolution enhanced rendering

Raster document coders are typically based on the use of a binary mask layer that efficiently encodes the text and graphic content. While these methods can yield much higher compression ratios than natural image compression methods, the binary representation tends to distort fine document details, such as thin lines, and text edges. In this paper, we describe a method for encoding and decoding the binary mask layer that substantially improves the decoded document quality at a fixed bit rate. This method, which we call resolution enhanced rendering (RER), works by adaptively dithering the encoded binary mask, and then applying a nonlinear predictor to decode a gray level mask at the same or higher resolution. We present experimental results illustrating that the RER method can substantially improve document quality at high compression ratios.

Image rendering for digital fax

Conventional halftoning methods such as error diffusion and ordered dithering are poorly suited to the compression of halftone images using the baseline fax compression schemes CCITT G3 and G4. This paper proposes an efficient and flexible solution for binary representation of mixed content documents using CCITT G3/G4 compression. The solution includes two variations which we refer to as FastFax and ReadableFax. FastFax performs edge detection and text detection by applying locally adaptive binary thresholding and combines the two detection results together. The FastFax algorithm produces an accurate representation of binary mixed document content with high compressibility using CCITT G3/G4 compression. ReadableFax is based on FastFax and applies clustered dot screening to background and halftone regions to enhance graphic content. Both methods provide accurate representation of image content while allowing for substantial compressibility, and provide a tradeoff between representation quality and bit rate.

Low-cost space-varying FIR filter architecture for computational imaging systems

Recent research demonstrates the advantage of designing electro-optical imaging systems by jointly optimizing the optical and digital subsystems. The optical systems designed using this joint approach intentionally introduce large and often space-varying optical aberrations that produce blurry optical images. Digital sharpening restores reduced contrast due to these intentional optical aberrations. Computational imaging systems designed in this fashion have several advantages including extended depth-of-field, lower system costs, and improved low-light performance. Currently, most consumer imaging systems lack the necessary computational resources to compensate for these optical systems with large aberrations in the digital processor. Hence, the exploitation of the advantages of the jointly designed computational imaging system requires low-complexity algorithms enabling space-varying sharpening. In this paper, we describe a low-cost algorithmic framework and associated hardware enabling the space-varying finite impulse response (FIR) sharpening required to restore largely aberrated optical images. Our framework leverages the space-varying properties of optical images formed using rotationally-symmetric optical lens elements. First, we describe an approach to leverage the rotational symmetry of the point spread function (PSF) about the optical axis allowing computational savings. Second, we employ a specially designed bank of sharpening filters tuned to the specific radial variation common to optical aberrations. We evaluate the computational efficiency and image quality achieved by using this low-cost space-varying FIR filter architecture.

Low-complexity digital filter bank for strongly aberrated optical systems having variable f-numbers

It has been shown that electro-optical imaging systems designed by integrating optics and digital processing provide system-level advantages such as extended depth-of-field and lower optical component costs. In such imaging systems, the strength of the optical aberration or blur can dramatically change with the f-number, and hence different digital filters in the subsequent digital processing are required to correct the captured images at different f-numbers. However, implementing such number of filters in hardware requires expensive computational resources, which in turn increase the overall system cost. In this paper, we propose a simple image filtering approach which uses a weighted sum of a set of component finite impulse response (FIR) filters to effectively apply a different composite FIR filter for each f-number. The simulation results demonstrate that our approach achieves desirable image quality for variable f-numbers, while substantially reducing the complexity in hardware implementation.

Low-complexity digital filter geometry for spherical coded imaging systems

Recent research in the area of electro-optical system design identified the benefits of spherical aberration for extending the depth-of-field of electro-optical imaging systems. In such imaging systems, spherical aberration is deliberately introduced by the optical system lowering system modulation transfer function (MTF) and then subsequently corrected using digital processing. Previous research, however, requires complex digital postprocessing algorithms severely limiting its applicability to only expensive systems. In this paper, we examine the ability of low-cost spatially invariant finite impulse response (FIR) digital filters to restore system MTF degraded by spherical aberration. We introduce an analytical model for choosing the minimum, and hence cheapest, FIR filter size capable of providing the critical level sharpening to render artifact-free images. We identify a robust quality criterion based on the post-processed MTF for developing this model. We demonstrate the reliability of the estimated model by showing simulated spherical coded imaging results. We also evaluate the hardware complexity of the FIR filters implemented for various spherical aberrations on a low-end Field-Programmable Gate Array (FPGA) platform.

46.4: Ghosting Reduction Using Digital Halftoning for Electrophoretic Displays

Ghosting on electrophoretic displays is an image artifact where previously displayed image content is visible in the current image. This paper introduces use of the previous image in a digital halftoning method to dither ghosting errors from grayscale imagery. Experiments show this method eliminates ghosting artifacts providing quality images without hardware modification.

Pixel and semantic capabilities from an image-object based document representation

This paper reports on novel and traditional pixel and semantic operations using a recently standardized document representation called JPM. The JPM representation uses compressed pixel arrays for all visible elements on a page. Separate data containers called boxes provide the layout and additional semantic information. JPM and related image-based document representation standards were designed to obtain the most rate efficient document compression. The authors, however, use this representation directly for operations other than compression typically performed either on pixel arrays or semantic forms. This paper describes the image representation used in the JPM standard and presents techniques to (1) perform traditional raster-based document analysis on the compressed data, (2) transmit semantically meaningful portions of compressed data between devices, (3) create multiple views from one compressed data stream, and (4) edit high resolution document images with only low resolution proxy images.