Quality is a number between 0-100, higher means better. To run it in lossy mode and reduce image quality to further optimize their sizes, the “-m quality” parameter can be set. It will display details on each image and how much their sizes were reduced as it progresses. You can read about why it’s important in the first part of this article here.īy default, it overwrites all files with optimized versions of the same image can simply be run on a batch of images by losslessly optimizing them and removing all comments and other non-image information: $ jpegoptim -s *.jpg It’s can also remove all meta information (Exif tags, color profiles) that should preferably be done to any image meant to be public. It supports both lossless and lossy operation and it’s smart enough to keep original images if this optimization process wouldn’t result in a reduced size. JpegOptim is a dedicated line tool that can be run both locally and even remotely on a server if installed, to process and reduce JPEG images. Using JpegOptim to optimize a batch of images We’re using -write here to save an intermittent step (the resized but not yet blurred image). The command that resulted in the image above was this: $ convert butterflies.jpg -resize 300x300 -write b1.jpg -sharpen 3x3 b2.jpg Radius sets the size of the area around each pixel to apply blurring, sigma is the size of the brush (how much a pixel is spread) around each processed pixel. Blur works by setting two parameters, radius x sigma. To apply sharpening to an image either as a standalone process or in combination with resizing the parameter “-sharpen” can be set.įirst, let’s make an image blurry by executing “convert -blur 3×3”. It also helps to recover details from blurry images (out of focus photos, for example) but it’s not always possible to put details back that were never there to begin with, so results may vary. It can also exaggerate the noise introduced by high compression (especially low-quality JPEG files) so the end result should always be reviewed for possible artifacts and it should not be used on heavily compressed images. It works by increasing the contract at the edges of the image, making it easier to see small details. Image sharpening is a great tool to combat this effect by applying this effect after the image is resized. When a picture is made smaller by resizing, small details often become blurry or lost simply because there are now fewer pixels to describe the same amount of information and relative to the size of objects, pixels just became bigger (or the objects smaller, depending on how you look at it) Next, we’re going to look at advanced usage of Image Magick techniques to keep better image details.
PICTURE OPTIMIZER SHARPENER HOW TO
You can read more about them here: How to optimize images on Linux Image sharpening basics In the first part of the article, we’ve looked at Image Magick usage, removing sensitive information from images, and geometry reduction techniques.
There are a few advanced tricks to optimize images for web consumption, such as sharpening them after size reduction and using dedicated command-line tools to reduce their sizes.
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