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When people talk about image optimization, they consider only the limited parameters offered by popular image editors, like the “Quality” slider, the number of colors in the palette, dithering and so on. Also, a few utilities, such as OptiPNG and jpegtran , manage to squeeze extra bytes out of image files. All of these are pretty well-known tools that provide Web developers and designers with straightforward techniques of image optimization. In this article, we’ll show you a different approach to image optimization, based on how image data is stored in different formats . Let’s start with the JPEG format and a simple technique called the eight-pixel grid. Eight-Pixel Grid As you already know, a JPEG image consists of a series of 8×8 pixel blocks, which can be especially visible if you set the JPEG “Quality” parameter too low. How does this help us with image optimization? Consider the following example: 32×32 pixels, Quality: 10 (in Photoshop), 396 bytes. Both white squares are the same size: 8×8 pixels. Although the Quality is set low, the lower-right corner looks fuzzy (as you might expect) and the upper-left corner looks nice and clean. How did that happen? To answer this, we need to look at this image under a grid: As you can see, the upper-left square is aligned into an eight-pixel grid, which ensures that the square looks sharp. When saved, the image is divided into blocks of 8×8 pixels, and each block is optimized independently . Because the lower-right square does not match the grid cell, the optimizer looks for color indexes averaged between black and white (in JPEG, each 8×8 block is encoded as a sine wave). This explains the fuzz. Many advanced utilities for JPEG optimization have this feature, which is called selective optimization and results in co-efficients of different quality in different image regions and more saved bytes. This technique is especially useful for saving rectangular objects. Let’s see how it works with a more practical image: 13.51 KB. 12.65 KB. In the first example, the microwave oven is randomly positioned. Before saving the second file, we align the image with the eight-pixel grid. Quality settings are the same for both: 55. Let’s take a closer look (the red lines mark the grid): As you can see, we’ve saved 1 KB of image data simply by positioning the image correctly. Also, we made the image a little “cleaner,” too. Color Optimization This technique is rather complicated and works only for certain kinds of images. But we’ll go over it anyway, if only to learn the theory. First, we need to know which color model is being used for the JPEG format. Most image formats are in the RGB color model, but JPEG can also be in YCbCr, which is widely used for television. RGB is similar to the HSV model (which should be familiar to most designers). It has three components: hue, saturation and value. The most important one for our purposes here is value, also known as lightness (optimizers tend to compress color channels but keep the value as high as possible because the human is most sensitive to it). Photoshop has a Lab color mode, which helps us better prepare the image for compression using the JPEG optimizer. Let’s stick with the microwave oven as our example. There is a fine net over the door, which is a perfect sample for our color optimization. After a simple compression, at a Quality of 55, the file weighs 64.39 KB. 990×405 pixels, Quality: 55 (in Photoshop), 64.39 KB. Larger version. Open the larger version of the image in Photoshop, and turn on Lab Color mode: Image > > Mode > > Lab Color. Lab mode is almost, but not quite, the same as HSV and YCbCr. The lightness channel contains information about the image’s lightness