Brian E. Cooper

Characterization of Electrophotographic Print Artifacts: Banding, Jitter, and Ghosting

Electrophotographic (EP) print banding, jitter, and ghosting artifacts are common sources of print quality degradation. Traditionally, the characterization of banding and jitter artifacts relies mainly on the assumption that the defect has either a horizontal or vertical orientation which permits the simple 1-D analysis of the defect profile. However, this assumption can easily be violated if a small amount of printer or scanner skew is introduced to the analyzed images. In some cases, the defect can inherently be neither vertical nor horizontal. In this case, unless the defect orientation has been accurately detected before analysis, the 1-D-based approaches could bias the estimation of the defect severity. In this paper, we present an approach to characterize the jitter and banding artifacts of unrestricted orientation using wavelet filtering and 2-D spectral analysis. We also present a new system for detecting and quantifying ghosting defects. It includes a design for a printed test pattern to emphasize the ghosting defect and facilitate further processing and analysis. Wavelet filtering and a template matching technique are used to detect the ghost location along and across the scanned test pattern. A new metric is developed to quantify ghosting based upon its contrast, shape, and location consistency. Our experimental results show that the proposed approaches provide objective measures that quantify EP defects with a rank ordering correlation coefficient of 0.8 to 0.98, as compared to the subjective assessment of print quality experts.

Adaptive image interpolation using a multilayer neural network

Image resizing is an important operation that is used extensively in document processing to magnify or reduce images. Standard approaches fit the original data with a continuous model and then resample this 2D function on a few sampling grid. These interpolation methods, however, apply an interpolation function indiscriminately to the whole image. The resulting document image suffers from objectionable moire patterns, edge blurring and aliasing. Therefore, image documents must often be segmented before other document processing techniques, such as filtering, resizing, and compression can be applied. In this paper, we present a new system to segment and label document images into text, halftone images, and background using feature extraction and unsupervised clustering. Once the segmentation is performed, a specific enhancement or interpolation kernel can be applied to each document component. In this paper, we demonstrate the power of our approach to segment document images into text, halftone, and background. The proposed filtering and interpolation method results in a noticeable improvement in the enhanced and resized image.

Evaluation of green noise masks for electrophotographic halftoning

Green noise is the mid-frequency component of white noise and has been shown to have visually pleasing attributes when applied to digital halftoning. Unlike blue noise dither patterns, which are composed exclusively of isolated pixels, green noise dither patterns are composed of pixel-clusters making them less susceptible to image degradation from non- ideal printing artifacts such as dot-loss. Clearly, these patterns reduce the spatial variation in tone produced by electrophotographic printers when printing a constant shade of gray, but to date, no study has been presented showing the amount of reduction. In this paper, we address this problem by studying the effects of changing the average cluster size in a green noise dither pattern, measuring the resulting spatial variations for a Lexmark Optra laser printer in 1200 dpi mode. The print quality is evaluated in terms of the visibility of printer mechanism noise and the average change in tone across the printed page.

Document image segmentation using a two-stage neural network

In this paper, we present a new system to segment and label document images into text, halftone images, and background using feature extraction and unsupervised clustering. Each pixel is assigned a feature pattern consisting of a scaled family of differential geometrical invariant features and texture features extracted from the cooccurence matrix. The invariant feature pattern is then assigned to a specific region using a two-stage neural network system. The first stage is a self-organizing principal components analysis (SOPCA) network that is used to project the feature vector onto its leading principal axes found by using principal components analysis. Using the SOPCA algorithm, we can train the SOPCA network to project our feature vector orthogonally onto the subspace spanned by the eigenvectors belonging to the largest eigenvalues. By doing that we ensure that the vector is represented by a reduced number of effective features. The next step is to cluster the output of the SOPCA network into different regions. This is accomplished using a self-organizing feature-map (SOFM) network. In this paper, we demonstrate the power of the SOPCA-SOFM approach to segment document images into text, halftone, and background.

Measurement of contributing attributes of perceived printer resolution.

Several measurable image quality attributes contribute to the perceived resolution of a printing system. These contributing attributes include addressability, sharpness, raggedness, spot size, and detail rendition capability. This paper summarizes the development of evaluation methods that will become the basis of ISO 29112, a standard for the objective measurement of monochrome printer resolution.

Photoconductor surface modeling for defect compensation based on printed images

Manufacturing imperfections of photoconductor (PC) drums in electrophotographic (EP) printers cause low- frequency artifacts that could produce objectionable non-uniformities in the final printouts. In this paper, we propose a technique to detect and quantify PC artifacts. Furthermore, we spatially model the PC drum surface for dynamic compensation of drum artifacts. After scanning printed pages of flat field areas, we apply a wavelet- based filtering technique to the scanned images to isolate the PC-related artifacts from other printing artifacts, based on the frequency, range, and direction of the PC defects. Prior knowledge of the PC circumference determines the printed area at each revolution of the drum for separate analysis. Applied to the filtered images, the expectation maximization (EM) algorithm models the PC defects as a mixture of Gaussians. We use the estimated parameters of the Gaussians to measure the severity of the defect. In addition, a 2-D polynomial fitting approach characterizes the spatial artifacts of the drum, by analyzing multiple revolutions of printed output. The experimental results show a high correlation of the modeled artifacts from different revolutions of a drum. This allows for generating a defect-compensating profile of the defective drum.

Automated measurement of printer effective addressability

When evaluating printer resolution, addressability is a key consideration. Addressability defines the maximum number of spots or samples within a given distance, independent of the size of the spots when printed. Effective addressability is the addressability demonstrated by the final, printed output. It is the minimum displacement possible between the centers of printed objects. In this paper, we present a measurement procedure for effective addressability that offers an automated way to experimentally determine the addressability of the printed output. It requires printing, scanning, and measuring a test target. The effective addressability test target contains two types of elements, repeated to fill the page: fiducial lines and line segments. The fiducial lines serve as a relative reference for the incremental displacements of the individual line segments, providing a way to tolerate larger-scale physical distortions in the printer. An ordinary reflection scanner captures the printed test target. By rotating the page on the scanner, it is possible to measure effective addressability well beyond the scanner’s sampling resolution. The measurement algorithm computes the distribution of incremental displacements, forming either a unimodal or bimodal histogram. In the latter case, the mean of the second (non-zero) peak indicates the effective addressability. In the former case, the printer successfully rendered the target’s resolution, requiring another iteration of the procedure after increasing the resolution of the test target. The algorithm automatically estimates whether the histogram is unimodal or bimodal and computes parameters describing the quality of the measured histogram. Several experiments have refined the test target and measurement procedure, including two round-robin evaluations by the ISO WG4 committee. Results include an analysis of approximately 150 printed samples. The effective addressability attribute and measurement procedure are included in ISO/IEC TS 29112, a technical specification that describes the objective measurement of printer resolution for monochrome electrophotographic printers.

On the analysis of wavelet-based approaches for print mottle artifacts

Print mottle is one of several attributes described in ISO/IEC DTS 24790, a draft technical specification for the measurement of image quality for monochrome printed output. It defines mottle as aperiodic fluctuations of lightness less than about 0.4 cycles per millimeter, a definition inherited from the latest official standard on printed image quality, ISO/IEC 13660. In a previous publication, we introduced a modification to the ISO/IEC 13660 mottle measurement algorithm that includes a band-pass, wavelet-based, filtering step to limit the contribution of high-frequency fluctuations including those introduced by print grain artifacts. This modification has improved the algorithm’s correlation with the subjective evaluation of experts who rated the severity of printed mottle artifacts. Seeking to improve upon the mottle algorithm in ISO/IEC 13660, the ISO 24790 committee evaluated several mottle metrics. This led to the selection of the above wavelet-based approach as the top candidate algorithm for inclusion in a future ISO/IEC standard. Recent experimental results from the ISO committee showed higher correlation between the wavelet-based approach and the subjective evaluation conducted by the ISO committee members based upon 25 samples covering a variety of printed mottle artifacts. In addition, we introduce an alternative approach for measuring mottle defects based on spatial frequency analysis of wavelet- filtered images. Our goal is to establish a link between the spatial-based mottle (ISO/IEC DTS 24790) approach and its equivalent frequency-based one in light of Parseval’s theorem. Our experimental results showed a high correlation between the spatial and frequency based approaches.

Segmentation and enhancement of digital copies using a new fuzzy clustering method

In this paper, we introduce a new system to segment and label document images into text, halftoned images, and background using a modified fuzzy c-means (FCM) algorithm. Each pixel is assigned a feature vector, extracted from edge information and gray level distribution. The feature pattern is then assigned to a specific region using the modified fuzzy c-means approach. In the process of minimizing the new objective function, the neighborhood effect acts as a regularizer and biases the solution towards piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by scanner noise.

Content-based document enhancement by fuzzy clustering with spatial constraints

In this paper, we present a new system to segment and label the contents of scanned documents as either text or image, using a modified fuzzy c-means (FCM) algorithm. Each pixel is assigned a feature pattern extracted from the gray level distribution and computed at different scales. The invariant feature pattern is then assigned to a specific region using fuzzy logic. Our algorithm is formulated by modifying the objective function of the standard FCM algorithm to allow the labeling of a pixel to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution towards piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by scanner noise.