How to Process Litho Into Continuous Tone

Continuous Tone Image

Image Quantization, Halftoning, and Printing

Ping Wah Wong , in Handbook of Image and Video Processing (Second Edition), 2005

3.3 Optimization Halftoning Techniques

Consider a distortion criterion d(f(n ₁, n ₂), b(n ₁, n ₂ )) between a continuous tone image f(n ₁, n ₂) and a halftone b(n ₁, n ₂). Given a specific f(n ₁, n ₂), we can find b(n ₁, n ₂) that minimizes the average distortion E[d(f(n ₁, n ₂), b(n ₁, n ₂))]. There are many methods for finding a signal to minimize a certain cost function. Here, any specific method for minimizing E[d(f(n ₁, n ₂), b(n ₁, n ₂))] will give rise to a halftoning algorithm.

Suppose we consider the frequency weighted squared error

$d\left(f\left(n_1,n_2\right),b\left(n_1,n_2\right)\right)={\left(v\left(n_1,n_2\right)*\left(b\left(n_1,n_2\right)-x\left(n_1,n_2\right)\right)\right)}^2.$

One way to find a halftone is to use a greedy minimization approach as follows: We scan the image f(n ₁, n ₂) in raster scan fashion. At each pixel location (n ₁, n ₂), we determine the binary output pixel as

$b\left(n_1,n_2\right)=\underset{b\left(n_1,n_2\right)\in \left[0,1\right]}{argmin}{\left(v\left(n_1,n_2\right)*\left(b\left(n_1,n_2\right)-x\left(n_1,n_2\right)\right)\right)}^2.$

Since there are only two possible values of b(n ₁, n ₂) for each (n ₁, n ₂), we can compute d(n ₁, n ₂), b(n ₁, n ₂)) for both values, and then pick the one that results in a smaller distortion. As long as v(n ₁, n ₂) has a causal support with respect to the scanning strategy, this minimization procedure can be performed very easily.

In general, greedy minimization does not generate output halftones of satisfactory quality. Since the greedy approach performs the minimization on a pixel by pixel basis with respect to a scanning strategy, there is no guarantee that it actually minimizes the overall average distortion. That is, any decision made by the greedy approach at a local pixel location will affect the local distortion of "future" pixels. Experimental results comparing greedy minimization and more sophisticated techniques indeed show that greedy minimization is far from optimal [24].

One method for generating halftones of excellent quality is direct binary search (DBS) [25]. The DBS algorithm goes through the image in multiple passes. In the kth pass, one uses the output halftone of the previous pass b_{k −1}(n ₁, n ₂) and the original continuous tone image f(n ₁, n ₂) to obtain an improved output halftone b_k (n ₁, n ₂). In the first pass, one needs an initial halftone b ₀(n ₁, n ₂) that can be generated, for example, using greedy optimization. In each pass over the image, the DBS algorithm attempts to improve the quality of the output using the following strategy: At each pixel location (n ₁, n ₂), one considers possible improvement to the output halftone by flipping (inverting) the current halftone pixel or by swapping the current halftone pixel with one of its eight neighbors. The halftone pixel b_k (n ₁, n ₂) at location (n ₁, n ₂) is chosen to be the one that results in the smallest distortion among the 10 possibilities (center pixel unchanged, center pixel inverted, center pixel swapped with one of its neighbors). Efficient methods for implementing the DBS algorithm have been suggested [25].

Other excellent methods for optimization based halftoning include tree coding [24], least square solution [26], combined halftoning-compression [27] and many others. Figure 10 shows a halftone generated using the tree coding algorithm [24] and the corresponding halftone power spectrum.

FIGURE 10. Boats image (a) halftoned using a tree coding algorithm [24] at 150 dots per inch, and (b) the halftone power spectrum.

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Fundamentals and Standards of Compression and Communication

Stephen P. Yanek , ... Joan E. Fetter , in Handbook of Medical Image Processing and Analysis (Second Edition), 2009

49.2.1 Joint Photographic Experts Group (JPEG) Compression

JPEG, formed as a joint committee of the International Standards Organization (ISO) and CCITT, focuses on standards for still image compression. The JPEG compression standard is designed for still color and grayscale images, otherwise known as continuous tone images , or images that are not restricted to dual-tone (black and white) only. Technologies such as color fax, scanners, and printers need a compression standard that can be implemented at acceptable price-to-performance ratios. The JPEG standard is published in two parts:

1.

The part that specifies the modes of operation, the interchange formats, and the encoder/decoder specified for these modes along with implementation guidelines;

2.

The part that describes compliance tests that determine whether the implementation of an encoder or decoder conforms to Part 1 to ensure interoperability of systems.

The JPEG compression standard has three levels of definition:

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Baseline system

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Extended system

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Special lossless function

A coding function performed by a device that converts analog signals to digital codes and digital codes to analog signals is called a codec. Every codec implements a baseline system, also known as the baseline sequential encoding. The codec performs analog sampling, encoding/decoding, and digital compression/decompression. The baseline system must satisfactorily decompress color images and handle resolutions ranging from 4 to 16 bits per pixel. At this level, the JPEG compression standard ensures that software, custom very large-scale integration (VLSI), and digital signal processing (DSP) implementations of JPEG produce compatible data. The extended system covers encoding aspects such as variable length encoding, progressive encoding, and the hierarchical mode of encoding. All of these encoding methods are extensions of the baseline sequential encoding. The special lossless function, also known as predictive loss coding, is used when loss in compressing the digital image is not acceptable.

There are four modes in JPEG:

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Sequential encoding

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Progressive encoding

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Hierarchical encoding

•

Lossless encoding

JPEG sequential encoding requirements dictate encoding in a left-to-right sequence and top-to-bottom sequence to ensure that each pixel is encoded only once. Progressive encoding is usually achieved by multiple scans. The image is decompressed so that a coarser image is displayed first and is filled in as more components of the image are decompressed. With hierarchical encoding, the image is compressed to multiple resolution levels so that lower resolution levels may be accessed for lower resolution target systems without having to decompress the entire image. With lossless encoding, the image is expected to provide full detail when decompressed.

JPEG and wavelet compression are compared in Figure 49.3, on 8-bit grayscale images, both at a compression ratio of 60 to 1. The top row shows chest X-ray images, and the bottom row presents typical magnified retina images. The image detail is retained better in the wavelet compressed image.

FIGURE 49.3. Effect of 60 to 1 compression on 8-bit grayscale images.

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Colour management and approval methods in lithographic printing

S. Wilkinson , in Colour Design, 2012

11.1.1.Early lithographic processes

To fully appreciate the advantages of current technology, it is important to understand how the process was managed previously. The lithographic process began with images being output on a process camera. The camera would produce images on litho film using filters to separate the cyan, magenta, yellow and black channels. The litho film would only record in black and white with a continuous tone image. The next step was to introduce a halftone screen to the negative or positive films. This was achieved by inserting a master screen sheet between the negative and new film and then exposing to light in a dark room in order to produce a set of CMYK films for plate making. The skills of the reprographic operators were key to the success of the process. During this stage, any retouching could be done to remove faults in the artwork or remove impurities, for example making flesh tones cleaner by removing cyan from them, a process now widely known as airbrushing on digital files. The process of proofing the CMYK films could now begin. This could range from making printing plates and 'wet' proofing on a special proofing press (another specialist skill) to out-putting a visual of the print by a branded proofing system such as Chroma-lin® by DuPont.

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Photographic Processes and Materials

P.S. Vincett , M.R.V. Sahyun , in Encyclopedia of Physical Science and Technology (Third Edition), 2003

VII.B.1 Printer Types

The design of the print head in a continuous ink-jet system is shown in Fig. 9 . In this example of a binary deflection system the drops are generated continuously by a piezoelectric device and pass between a pair of charging electrodes which, depending on the signal applied, may or may not apply a charge to a particular drop (or set of drops). A deflection plate deflects the charged drops electrostatically to the gutter, where the ink is collected for reuse, while the uncharged drops proceed to the paper. There are also similarly functioning multiple deflection systems, wherein the charge applied to the droplets is variable; the angle of deflection of the droplets on passing through the deflection plate is likewise variable. The drops can accordingly be deflected to different spots on the paper. Both types of devices are used for industrial coding, marking, and labeling applications. The multiple deflection continuous ink-jet method is particularly suitable for high-quality, continuous tone images, e.g., in the graphic arts market. Very large images, up to billboard size, can be produced using continuous ink-jet technology.

FIGURE 9. A binary deflection continuous ink jet print head. [From Le, H. P. (1998). J. Imaging Sci. Technol. 42, 49–62; by permission of the copyright holder, the Society for Imaging Science and Technology.]

Drop-on-demand ink-jet print heads are less complex and are commonly used in printers for the office and home markets. Evolution of the drop-on-demand technology to increasingly small droplet sizes (currently picoliters) has enabled extremely high quality to be obtained with these printers, as well, though not with as high speed as the continuous ink-jet printers. Most drop-on-demand ink jet printers utilize either a thermal or a piezoelectric driver. The thermal ink jet is considered to be the most reliable method on the market at this time. A diagram of a representative thermal ink jet is shown in Fig. 10. The device involves a resistive heating element which turns an electrical pulse into a heat pulse. The heat pulse causes the ink to boil where it is in contact with the element, and the bubble-forces a droplet of the ink out of the orifice and impels it onto the paper. It is this bubble-forming feature of the print head that inspired one manufacturer of printers utilizing this method (Canon) to adopt the trade name BubbleJet. Several other manufacturers, e.g., Lexmark and Hewlett-Packard, however, utilize this printing method with only minor variations.

FIGURE 10. A thermal ink jet print head, showing the evolution of bubble formed from the ink heated by the resistive heating element. [From Le, H. P. (1998). J. Imaging Sci. Technol. 42, 49–62; by permission of the copyright holder, the Society for Imaging Science and Technology.]

In the piezoelectric method, a piezoceramic deforms in response to the electronic signal and forces ink out of the orifice. Different manufacturers of these devices utilize different geometries, giving rise to so-called squeeze, bend, push, and shear modes of operation. Squeeze mode print heads are, by now, more a matter of historical interest, and shear mode devices have not yet made a significant commercial impact. Bend and push mode devices, illustrated in Fig. 11, are both commonplace designs in ink-jet printers offered to office, home, and commercial printing markets by a number of manufacturers, including Dataproducts, Epson, Sharp, Tektronix, and Xerox.

FIGURE 11. Two geometries of piezoelectric driven drop-on-demand print heads: bend-mode (top) and push-mode (bottom). (From Le, H. P. (1998). J. Imaging Sci. Technol. 42, 49–62; by permission of the copyright holder, the Society for Imaging Science and Technology.]

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Lossless Image Compression

Lina J. Karam , in The Essential Guide to Image Processing, 2009

16.5.1 CALIC

CALIC represents one of the best performing practical and general purpose lossless image coding techniques.

CALIC encodes and decodes an image in raster scan order with a single pass through the image. For the purposes of context modeling and prediction, the coding process uses a neighborhood of pixel values taken only from the previous two rows of the image. Consequently, the encoding and decoding algorithms require a buffer that holds only two rows of pixels that immediately precede the current pixel. Figure 16.5 presents a schematic description of the encoding process in CALIC. Decoding is achieved by the reverse process. As shown in Fig. 16.5, CALIC operates in two modes: binary mode and continuous-tone mode. This allows the CALIC system to distinguish between binary and continuous-tone images on a local, rather than a global, basis. This distinction between the two modes is important due to the vastly different compression methodologies employed within each mode. The former uses predictive coding, whereas the latter codes pixel values directly. CALIC selects one of the two modes depending on whether or not the local neighborhood of the current pixel has more than two distinct pixel values. The two-mode design contributes to the universality and robustness of CALIC over a wide range of images.

FIGURE 16.5. Schematic description of CALIC (Courtesy of Nasir Memon).

In the binary mode, a context-based adaptive ternary arithmetic coder is used to code three symbols, including an escape symbol. In the continuous-tone mode, the system has four major integrated components: prediction, context selection and quantization, context-based bias cancellation of prediction errors, and conditional entropy coding of prediction errors. In the prediction step, a gradient-adjusted prediction $\widehat{y}$ of the current pixel y is made. The predicted value $\widehat{y}$ is further adjusted via a bias cancellation procedure that involves an error feedback loop of one-step delay. The feedback value is the sample mean of prediction errors $\overline{e}$ conditioned on the current context. This results in an adaptive, context-based, nonlinear predictor $\check{y}=\widehat{y}+\overline{e}$ . In Fig. 16.5, these operations correspond to the blocks of, "context quantization", "error modeling," and the error feedback loop.

The bias corrected prediction error $\check{y}$ is finally entropy coded based on a few estimated conditional probabilities in different conditioning states or coding contexts. A small number of coding contexts are generated by context quantization. The context quantizer partitions prediction error terms into a few classes by the expected error magnitude. The described procedures in relation to the system are identified by the blocks of "context quantization" and "conditional probabilities estimation" in Fig. 16.5. The details of this context quantization scheme in association with entropy coding are given in [9].

CALIC has also been extended to exploit interband correlations found in multiband images like color images, multispectral images, and 3D medical images. Interband CALIC can give 10% to 30% improvement over intraband CALIC, depending on the type of image. Table 16.5 shows bit rates achieved with intraband and interband CALIC on a set of multiband images. For the sake of comparison, results obtained with JPEG-LS are also included.

TABLE 16.5. Lossless bit rates with Intraband and Interband CALIC (Courtesy of Nasir Memon).

Image	JPEG-LS	Intraband CALIC	Interband CALIC
band	3.36	3.20	2.72
aerial	4.01	3.78	3.47
cats	2.59	2.49	1.81
water	1.79	1.74	1.51
cmpnd1	1.30	1.21	1.02
cmpnd2	1.35	1.22	0.92
chart	2.74	2.62	2.58
ridgely	3.03	2.91	2.72

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Lossless Coding

Lina J. Karam , in Handbook of Image and Video Processing (Second Edition), 2005

5.1 CALIC

CALIC (Context-based, Adaptive, Lossless Image Codec) represents one of the best performing practical and general purpose lossless image coding techniques.

CALIC encodes and decodes an image in raster scan order with a single pass through the image. For the purposes of context modeling and prediction, the coding process uses a neighborhood of pixel values taken only from the previous two rows of the image. Consequently, the encoding and decoding algorithms require a buffer that holds only two rows of pixels that immediately precede the current pixel. Figure 5 presents a schematic description of the encoding process in CALIC. Decoding is achieved by the reverse process. As shown in Fig. 5, CALIC operates in two modes: binary mode and continuous-tone mode. This allows the CALIC system to distinguish between binary and continuous-tone images on a local, rather than a global, basis. This distinction between the two modes is important due to the vastly different compression methodologies employed within each mode. The former uses predictive coding, whereas the latter codes pixel values directly. CALIC selects one of the two modes depending on whether or not the local neighborhood of the current pixel has more than two distinct pixel values. The two-mode design contributes to the universality and robustness of CALIC over a wide range of images.

FIGURE 5. Schematic description of CALIC

(courtesy of Nasir Memon).

In the binary mode, a context-based adaptive ternary arithmetic coder is used to code three symbols, including an escape symbol. In the continuous-tone mode, the system has four major integrated components: prediction, context selection and quantization, context-based bias cancellation of prediction errors, and conditional entropy coding of prediction errors. In the prediction step, a gradient-adjusted prediction (GAP) $\hat{y}$ of the current pixel y is made. The predicted value $\hat{y}$ is further adjusted via a bias cancellation procedure that involves an error feedback loop of one-step delay. The feedback value is the sample mean of prediction errors ē conditioned on the current context. This results in an adaptive, context-based, non-linear predictor $\hat{y}=\hat{y}+\tilde{e}$ . In Fig. 5, these operations correspond to the blocks of "context quantization", "error modeling", and the error feedback loop.

The bias corrected prediction error $\stackrel{⌣}{y}$ is finally entropy coded based on a few estimated conditional probabilities in different conditioning states or coding contexts. A small number of coding contexts are generated by context quantization. The context quantizer partitions prediction error terms into few classes by the expected error magnitude. The described procedures in relation to the system are identified by the blocks of "context quantization" and "conditional probabilities estimation" in Fig. 5. The details of this context quantization scheme in association with entropy coding are given in [6].

CALIC has also been extended to exploit inter-band correlations found in multi-band images like color images, multispectral images and 3-D medical images. Inter-band CALIC can give 10 to 30% improvement over intra-band CALIC, depending on the type of image. Table 5 shows bit-rates achieved with intra-band and inter-band CALIC on a set of multi-band images. For the sake of comparison, results obtained with JPEG-LS are also included.

TABLE 5. Lossless bit rates with intra-band and inter-band CALIC

Image	JPEG-LS	Intra-band CALIC	Inter-band CALIC
Band	3.36	3.20	2.72
Aerial	4.01	3.78	3.47
Cats	2.59	2.49	1.81
Water	1.79	1.74	1.51
Cmpndl	1.30	1.21	1.02
Cmpnd2	1.35	1.22	0.92
Chart	2.74	2.62	2.58
Ridgely	3.03	2.91	2.72

(courtesy of Nasir Memon).

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