Understanding Advanced Image Editing Tools |
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Bibble provides the following advanced image editing tools to control monitor calibration, the color hue, saturation and brightness, white balance, and exposure of the image. Click the following links for detailed procedures on how to adjust color values. For conceptual information on advanced color editing concepts, see the following subsections. Understanding Color Management and Monitor Proofing Calibrating your monitor adjusts the settings that describe how the monitor reproduces color. Calibrating the monitor ensures that the colors you see on the monitor will match up with the colors reproduced by the print output device. Note: To calibrate accurately with an output device, you might need to consult your prepress service provider. When you calibrate your monitor, you set it to a known standard. For example, you might calibrate the monitor to have a white point color temperature range of 5000-6500 Kelvin, which is a common graphic standard. Most digital cameras auto-calibrate (and adjust) their white balance value based on lighting conditions. If your camera provides an ICC calibration profile, you should load this profile and use it when proofing images on screen. Understanding White Balance and Color Temperature When shooting a photograph, it is important to match the white balance setting of your camera to the color temperature of the light on the subject. For example, if you shoot a picture outdoors on a sunny day, your camera probably has a white balance setting called "daylight" that matches the Kelvin color temperature setting of the subject illuminated by sunlight. Although it is not necessary to know the exact Kelvin color temperatures of light, your images will look better if you match the white balance setting to the lighting conditions at the time the photograph was taken. Bibble provides numerous white balance settings that can be applied to the image. For example, if a shot was taken in fluorescent light, but the camera white balance was set to "daylight", Bibble can correct the colors by applying the "fluorescent" white balance setting. Also, Bibble lets you set a custom Kelvin color temperature value so that you can fine-tune the white balance for best results. The following table correlates Kelvin degrees to common light sources:
Note that certain light sources (like fluorescent lights) have a wide range of temperature variance. Understanding Exposure and Tone Curves Exposure specifies the amount of light captured by your digital camera's sensor. If an image is over-exposed, the image appears washed out. If the image is under-exposed, the image appears too dark. Bibble provides tools to correct exposure problems with original images. The tone of an image is based on the hue, saturation, and brightness of the colors. Hue measures the color reflected from objects in the image, saturation measures the strength of the colors, and brightness measures the lightness of the image. Understanding Curves and Histograms Working with Curves Curves provide a mechanism to adjust the tonal range of an image. Instead of making adjustments to only the highlights, shadows, and midtones, when you work with curves you can adjust any point along the entire color and tonal range. Bibble lets you click on the curve to set numerous constant points that lock in certain values, but also provide the flexibility to adjust tonal ranges between constant points. For example, you can fix the highlight and shadow tones by setting anchor points at the one-quarter and three-quarter positions along the curve, but you can adjust every midtone point between the anchors by moving the curve. For greater color editing control, you can set the curves for the red, blue, and green channels to make precise adjustments to individual color channels in an image. Working with Histograms Histograms show you how the pixels in an image are distributed by graphing the number of pixels at each color intensity level. The vertical axis represents the total number of pixels. The horizontal axis represents the intensity levels from darkest (left) to brightest (right). For example, if the graph shows higher numbers of pixels in the left portion of the horizontal axis, then the image has more intensity in the shadows. Likewise, if the image shows high pixel levels in the middle or the right of the axis, the image has more intensity in the midtones or the highlights, respectively. As with curves, you can adjust the tonal range of the RGB channels simultaneously, or you can make adjustment to each channel separately. Understanding Highlight Recovery Highlight recovery restores color data to overexposed areas of your images. Bibble's highlight recovery tools can add up to a 1/2 stop or more of detail to the overexposed portions of the image without changing the overall exposure or color balance. If one or more color channels in your image are blown out, Bibble's color sensor is able to determine if one of the other less sensitive channels contains valid data. Utilizing the remaining data, Bibble analyzes the image and provides data for the lost channels. When data from only one channel is missing, Luma and Chroma information is recovered. When data for only one channel is present, only Luma is recovered. In both cases, you will have more color information than the data present in the original image. Understanding Fill Lighting Fill light illuminates the areas of an image that are in the shadows. The amount of fill light softens shadows by brightening the side of the subject that is not in direct sunlight or exposed to the main studio light. Bibble's fill light feature lets you control the amount of fill light you add to the overall image. You can set the controls so that you add fill light only to the darkest areas of the image, or you can set the tools to apply the fill light affects to all areas of the image. Understanding Noise Ninja Noise Ninja™ is a state-of-the-art noise reduction system developed by the PictureCode company that can be used as a stand-alone product or through the Bibble interface as a Bibble plug-in. Noise Ninja technology achieves an unprecedented balance between noise suppression and detail preservation, providing natural-looking results without artifacts. To use the Bibble plug-in for Noise Ninja, you must be a registered user of Noise Ninja. For more information about Noise Ninja (or to purchase a licence), visit the PictureCode company web site at http://www.picturecode.com. Understanding Lens Distortion and Correction Lens distortion refers to any imperfection in the image that is projected on your camera's sensor at the time you press the shutter release. While zoom lenses with a large range of focal lengths show the greatest distortion, even fixed-focal length - or "prime" - lenses can exhibit some types of distortion. The three most common types of lens distortion can quickly and easily be corrected in Bibble. Barrel and Pin-Cushion distortion is commonly referred to simply as Lens Distortion. This is caused from non-uniform magnification of the image from the outside of the image (perimeter) to the center. Barrel distortion refers to magnification that diminished towards the edges of the image resulting in a image that looks rounded, like a barrel. Pin cushion appears as an image that looks pinched or narrowed at the sides. Each lens has its own Barrel and Pin-Cushion characteristics, and by analyzing a set of images from a lens at all focal lengths, this distortion can be removed. Chromatic Aberration distortion (known a CA distortion) is a result of non-uniform bending of light of varying color (wavelength) as it passes through a lens. Zoom lenses, particularly at their widest and longest focal lengths, exhibit the most severe distortion. This distortion appears most at image corners in high-contrast areas, like branches of a tree silhouetted against a bright sky, and is seen as uneven colors around the details of an image. This is typically called color fringing, and is mostly seen in purple colors. CA distortion can be removed by adjusting the data for the colors that show the most distortion. Vignetting is the darkening of corners of an image due to light fall-off, and can be caused by optics (the lens itself), the sensor (many sensors are less sensitive to light that hits the sensor at an angle) or from other causes like a filter or lens hood that shades the corners of an image. Vignetting can be corrected by lightening the only the corners of an image. However, some chose to add or enhance vignetting as an artistic effect. Thus Bibble allows you to darken the corners of an image, artificially adding vignetting. Understanding Spot and Patch Healing It is very easy to include distracting details in an photo. With Bibble's Spot Healing Tool, you can remove these small portions of your image to conceal blemishes or to remove a distracting bird from a clear sky. This tool can operate like a Cloning tool - copying one part of an image over a blemish, or it can operate like a Heal tool - cleaning a part of your image without needing a "source" to copy from. Understanding Sensor Correction Sometimes a pixel in digital cameras becomes "Stuck" - meaning that instead of accurately recording image data, it records a fixed color or brightness regardless of the image being captured. If the pixel is always set to black, its called a Dead Pixel. If the pixel is always set to a single color, is called a Hot pixel. Camera makers typically will allow a small number of Stuck Pixels on their sensors before they will replace the camera under warranty. In most cases, these individual pixels are not noticeable, and do not impact image quality. However, if a Hot pixel that is always seen as white appears in deep shadows it will be visible and distracting. Likewise, a Dead pixel on bright sky or other highlighted area will also detract from the overall image quality. If your camera exhibits one or more of these pixels, you can enable "Stuck Pixel Correction" to automatically remove these blemishes from your images. Once enabled, the correction is quick and automatic, and your final output image will be free of distracting pixels. |