It's not a bad solution, but I don't think it's the best solution. Obviously, the thing that sticks out as being the most inefficient is that the small (or default) image is always loaded. This means that for tablets and desktop browsers there will be an extra image request that is never used. Not too bad, but on a page with a lot of images this could add up.
Another thing I would suggest the team think of is not loading images by browser width only. If we're tying these libraries to the idea that they improve performance by optimizing which images are loaded - so that you only transfer the necessary amount of KB per page view - browser width is a bit removed from what you want. What you actually want to measure is the user's network speed (which can be done with libraries like Foresight.js : https://github.com/adamdbradley/foresight.js). Loading large images on a slow network isn't going to be good for performance. By using both browser size and network speed, you can optimize images for mobile devices over 3G or those connected to WiFi. Or desktops on broadband versus desktops on 56k modems.
I haven't personally tried it yet, but it seems the best option out there at the moment is the Capturing polyfill by Mozilla (https://hacks.mozilla.org/2013/03/capturing-improving-perfor...).
Another thing I would suggest the team think of is not loading images by browser width only. If we're tying these libraries to the idea that they improve performance by optimizing which images are loaded - so that you only transfer the necessary amount of KB per page view - browser width is a bit removed from what you want. What you actually want to measure is the user's network speed (which can be done with libraries like Foresight.js : https://github.com/adamdbradley/foresight.js). Loading large images on a slow network isn't going to be good for performance. By using both browser size and network speed, you can optimize images for mobile devices over 3G or those connected to WiFi. Or desktops on broadband versus desktops on 56k modems.