![]() Reduced image from 11.1 MB to 8.5 MB (23% reduction), no batch option Reduced image from 11.1 MB to 4.2 MB (62% reduction), no batch option Reduced image from 11.1 MB to 7.6 MB (32% reduction), batch option For the single large 11.1 MB JPEG developer’s portfolio background photo these were the results: ![]() Note: all PNGs were converted to JPEGs for web hosting. For the overall reduction of developer’s portfolio (jpeg but mostly pngs) site. Reduced image from 322.1 MB to 98.4 MB (69% reduction) of actual disk size. With mobile providers charging about $10 a gig puts this person’s site at load cost of $3.Ĭhrome reports very close values w/ optimized image The nasty truth became apparent when I opened up my Chrome network tab and found the site was loading over 300 MBs of data. I ran across a photographer’s portfolio website where they had a set of 4 different carousels showing off a total of 41 photos. Simply put: smaller image size => better. This lack of proper image compression causes cascading impacts: ballooning storage buckets, increase data transfer cost for both you and your user’s data plan, and, most critical, the negative impacts the user experiences with poor load times - especially in places with only 3g connections (or less). What’s worse is that these images haven’t been optimized for the web. The basic we’ll look at today is simple: optimizing images for the web. ![]() ![]() However, not enough of these engineers look at some basics that will immediately improve the load times of web apps, improve user experience while also reducing data usage and storage costs. Icon made by Smashicons from The majority of software engineers I know love to focus on the latest and greatest framework, technology, or library. ![]()
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