![]() ![]() The Cloudinary algorithm can then check if a different format (e.g. ![]() Take your image optimization to the next level by combining automatic image quality selection with automatic image format selection. ![]() The image is initially displayed with a quality setting of 100, and clicking on one of the buttons below the image will display the image using that particular quality setting.Īutomatic image quality – with automatic image format The interactive image example below shows the effect that the various auto quality settings have on the visual quality of the same beach image used in the first example above, also scaled down to a width of 600 pixels. ![]() To address these and other considerations, you can influence how aggressively the quality automation algorithm reduces the file size, by adding an extra value to the q_auto parameter that describes the level of visual quality to aim for (best, good, eco, or low). Will the image receive a lot of traffic and therefore bandwidth considerations become even more important?.Do you sell images, or display high resolution images for printing, so that the visual quality is something you don’t want to compromise at all?.Is the image used as a thumbnail link to a high quality version?.How you ultimately use the image may also influence the quality/size trade-off decision. Click on the quality buttons below the image to see how different quality settings impact the image quality. The image is scaled down to a width of 600 pixels and initially displayed with a quality setting of 100. The interactive image example below shows the effect that the various quality settings have on an image’s visual quality. However, this time consuming process is not efficient when you have to fine tune a huge number of images, never mind the impossibility of manually finding the optimal quality if you have a lot of user-uploaded images to display. Taking the above points into consideration is all well and good when you have the time to fiddle with each image and find the optimal quality setting that gives the biggest saving on file size without affecting the visual quality. An image’s quality setting should be optimized for each of the resized images and not just one quality setting for all sizes. 75), the same image scaled to a different size may have visual artifacts with the same quality setting. Where an image of a certain size may display perfectly with a given quality setting (e.g. The size of the image can require a different quality setting for each size displayed.60), another image with different content may have visual artifacts with the same quality setting. For example, does the image contain a huge range of colors? A large degree of complicated detail? Smooth color gradients? Selecting a single default quality setting for all your images fails to account for these differences in images: where one image may display perfectly with a given quality setting (e.g. The contents of an image determines to a large degree how well it can be compressed before losing visual quality.70) also means different things in different contexts and for different formats. Different algorithms and formats tend to implement the settings in different ways, so any specific quality (e.g. The image quality setting is usually specified on a sliding scale between 0 and 100, but what does this number actually represent for different image formats such as JPEG, WebP, and PNG? The compression algorithms used are complex and varied, so it’s not always known what these quality settings actually mean without a lot of experimentation.So, what’s the standard quality and encoding setting that works for all images? Unfortunately, there is no single setting that is optimal for all images, and simply lowering the image quality to a new setting is problematic: Precise adjustment of the compression level, complemented by fine-tuning of the encoding settings, can significantly reduce the file size without any degradation noticeable to the human eye. But adjusting the quality compression level doesn’t always lead to a loss of visual quality. On the other hand, a lot of developers are afraid to use aggressive compression for fear of degrading the visual quality, and tend to err on the side of caution, assigning higher quality values than are really needed. Compression can be very effective at reducing the size of the image, and besides lowering the costs of storage space and bandwidth, a reduced image size goes a long way to retaining your users’ attention with faster, smaller downloads. How much lower, and whether low enough to make a difference visually, depends on the image. One of the most important things to know about compressing image files is that a smaller file size comes at the cost of a lower image quality. ![]()
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