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We recently worked with the team at Cloudinary to help build and evaluate a better image compression and quality measurements. The results of Cloudinary’s work on Scale were very insightful and we wanted to share it broadly to demonstrate how more companies can leverage human judgments to build high quality features. Without further ado, <a href="https://cloudinary.com/blog/author/tal_lev_ami" rel="noopener noreferrer" target="_blank">Tal Lev Ami</a>, CTO of Cloudinary, wanted to write about how they used Scale: <blockquote> Here at Cloudinary, we provide a cloud-based tool that enables our users to</blockquote><blockquote> compress images and video for their websites and apps. Our goal is to</blockquote><blockquote> preserve the visual integrity of the content, but deliver the smallest file</blockquote><blockquote> size to any device or browser to ultimately optimize website performance and</blockquote><blockquote> end user satisfaction.</blockquote> <blockquote> One of the hallmarks of the Cloudinary solution is the ability to automate</blockquote><blockquote> many functions of image compression, so that developers don’t have to spend</blockquote><blockquote> time tweaking each photo and making multiple copies of different sizes and</blockquote><blockquote> resolutions to fit every possible scenario. Compression algorithms can be</blockquote><blockquote> tricky because they’re trying to make changes that have the smallest visual</blockquote><blockquote> impact, but different images can react differently to compression.</blockquote> <blockquote> As we were developing the algorithm for our “q_auto” capabilities — which</blockquote><blockquote> strikes a balance between visual quality and file size — we needed to test</blockquote><blockquote> it to understand how the resulting images compared to the human eye. Enter</blockquote><blockquote> Scale.</blockquote> <blockquote> Many image compression formats — like JPEG 2000 and JPEG XR — have been</blockquote><blockquote> tweaked to score well on particular metrics, such as peak signal-to-noise</blockquote><blockquote> ratio (PSNR). But these don’t always correlate with human perception on</blockquote><blockquote> image quality.</blockquote> <blockquote> We leveraged Scale to compare pairs of images and give us perspective on</blockquote><blockquote> which image was liked most by humans. With Scale, we did a variety of tests,</blockquote><blockquote> comparing several formats, including WebP, JPEG 2000, JPEG XR (lossy) Lepton</blockquote><blockquote> (MozJPEG, recompressed with Lepton), FLIF, BPG, Daala, and PNG8</blockquote><blockquote> (pngquant+optipng). We also were able to get input on the difference between</blockquote><blockquote> the uncompressed original image vs. a compressed version.</blockquote> <blockquote> Scale enabled us to create A/B comparisons that were viewed by human</blockquote><blockquote> observers. We submitted over 4,000 image comparisons to Scale, sending at</blockquote><blockquote> least four independent Scale requests for each pair of image. This resulted</blockquote><blockquote> in at least eight actual human comparisons for each pair of images. The</blockquote><blockquote> outcome of these comparisons were evaluated beside other perceptual metrics</blockquote><blockquote> such as PSNR, Google’s Butteraugli, DSSIM (Structural (Dis)Similarity) and a</blockquote><blockquote> new metric Cloudinary developed called</blockquote><blockquote> <a href="https://cloudinary.com/blog/detecting_the_psychovisual_impact_of_compression_related_artifacts_using_ssimulacra" rel="noopener noreferrer" target="_blank">SSIMULACRA (Structural SIMilarity Unveiling Local And Compression Related</a></blockquote><blockquote><a href="https://cloudinary.com/blog/detecting_the_psychovisual_impact_of_compression_related_artifacts_using_ssimulacra" rel="noopener noreferrer" target="_blank"> Artifacts)</a>.</blockquote> <blockquote> The results showed that overall, PSNR is “correct” in only 67 percent of the</blockquote><blockquote> cases. Butteraugli gets it right in 80 percent of the cases, and DSSIM in 82</blockquote><blockquote> percent of the cases. Our new metric, SSIMULACRA, agrees with human</blockquote><blockquote> judgments in 87 percent of the cases. Looking just at the high-confidence</blockquote><blockquote> human judgments, we found about 78 percent agreement for PSNR, 91 percent</blockquote><blockquote> for both Butteraugli and DSSIM, and almost 98 percent agreement for</blockquote><blockquote> SSIMULACRA. You can</blockquote><blockquote> <a href="https://cloudinary.com/blog/detecting_the_psychovisual_impact_of_compression_related_artifacts_using_ssimulacra" rel="noopener noreferrer" target="_blank">read more about SSIMULACRA and these results on the Cloudinary blog</a>. Or if you want to give it a try:</blockquote><blockquote> <a href="https://github.com/cloudinary/ssimulacra" rel="noopener noreferrer" target="_blank">SSIMULACRA is free and open-source software!</a></blockquote> <blockquote> The results of Scale comparisons gave us useful data points to validate our</blockquote><blockquote> metrics and provided more insights into the compression benchmarks we are</blockquote><blockquote> running and the comparison of various image formats. And from these insights</blockquote><blockquote> we were able to improve our visual perception metrics and fine-tune our</blockquote><blockquote> “q_auto” functionality so we know how aggressively we can compress images.</blockquote> <blockquote> Through this process we were impressed not only by the useful data points</blockquote><blockquote> derived from the Scale, but also the great support we got from the company</blockquote><blockquote> and the product’s ease-of-use, all which came at a reasonable price.</blockquote>

We recently worked with the team at Cloudinary to help build and evaluate a better image compression and quality measurements.

Image Compression: Cloudinary Relies on Scale Technology

Guest Post: Cloudinary Uses Scale to Focus on Image Compression

We are excited to&nbsp;<a href="https://www.wsj.com/articles/scale-helps-engineers-outsource-microtasks-1495452603" rel="noopener noreferrer" target="_blank">announce</a>&nbsp;that we have raised a $4.5 million Series A round of funding led by Accel. Along with this funding, Accel’s Daniel Levine has joined Scale’s board. The funding will be used to invest in our rapid growth, expand our offerings, and grow our team. When we started Scale ten months ago, our goal was to build the simplest API for training data. Since then we have released six APIs (<a href="https://scale.com/image-annotation" rel="noopener noreferrer" target="_blank">Image Annotation</a>,&nbsp;<a href="https://scale.com/ocr-transcription" rel="noopener noreferrer" target="_blank">OCR Transcription</a>,&nbsp;<a href="https://scale.com/categorization" rel="noopener noreferrer" target="_blank">Categorization</a>,&nbsp;<a href="https://scale.com/comparison" rel="noopener noreferrer" target="_blank">Comparison</a>, and&nbsp;<a href="https://scale.com/data-collection" rel="noopener noreferrer" target="_blank">Data Collection</a>), and we process millions of API requests each month. This success has confirmed our conviction that there is no better time than now to build Scale. We are currently at an inflection point in the age of artificial intelligence (AI). Just like software before it, AI will have an enormous effect on productivity and enable incredible new technologies. Scale works with many companies and technologies at the forefront of this shift, including self-driving cars, intelligent assistants, and voice analytics. Our customers agree that integrating AI with accurate human intelligence is crucial to building reliable AI technology. As a result, we believe Scale will be a foundation for the next wave of development in AI. This financing will allow us to grow our engineering, operations, support, marketing, and sales teams to continue on our journey to make our customers more productive and efficient. We’re committed to continuously improving our costs, speed, and quality and to providing a quality customer experience. We’d also like to thank our colleagues, customers, partners, and investors for supporting on this journey to build APIs for human intelligence. We look forward to continuing to support companies and developers in creating innovative software in the age of AI. This is just the beginning. We are actively hiring exceptional engineers, designers, AI researchers, and more. If you are interested in joining Scale, check out our&nbsp;<a href="https://scale.com/about#jobs" rel="noopener noreferrer" target="_blank">careers page</a>.

Announcing Our Series A With Accel

One of the largest automobile companies in the world uses Scale to help build self-driving cars. Self-driving cars (SDCs) are one of the most exciting technologies being developed today. If they become a reality, the benefits that come will significantly impact our lives in several ways:<ul><li>Lives will be saved</li><li>Transportation speeds go up → Time is saved.</li><li>People will be freed up to be productive or enjoy entertainment on the road</li></ul>One of the big challenges for SDCs is&nbsp;extremely&nbsp;high-stakes computer vision. SDCs need to quickly recognize various road conditions, animals, people, other cars, etc. They need to understand how these things are behaving and how they’re likely to behave in the near future. Computer vision, by way of deep learning, has come a long way recently, but it needs to keep improving to ensure SDCs are safe and reliable. SDC companies are using Scale to tag objects by passing images through Scale’s&nbsp;<a href="http://scale.com/image-recognition" rel="noopener noreferrer" target="_blank">Image Recognition API</a>. Scalers label cars, people, signs, traffic lights, etc. to help train their models. An example call would look something like this: <pre class="ql-syntax" spellcheck="false">import scaleapi
client = scaleapi.ScaleClient('SCALE_API_KEY')
client.create_annotation_task(
 instruction='Draw a tight box around each **car** and **pedestrian**.'
 attachment_type='image',
 attachment='https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/e2bea5ad-8ea7-4726-6f9e-9e99264c0600/public',
 objects_to_annotate=['car', 'pedestrian'],
 with_labels=True,
 callback_url='http://www.example.com/callback'
)
</pre>At Scale, we deeply believe in investing in our customer experience. It’s great to have customers that push us, particularly in ways that benefit all customers. We worked closely with the automobile company to make sure our quality was up to their standards, while maintaining a reasonable cost. Some examples of specific things we did to deliver for the customer:<ul><li>Our Scalers take online tests to determine their skill level at a specific task. We improved the Image Recognition test to ensure that Scalers were bounding objects within a pixel of accuracy on each image. Not every customer needs that accuracy, but it’s nice to have nonetheless!</li><li>We invested in the software tool our Scalers use to annotate images to improve their UX. Improving the performance and layout of the tool made a big difference. We took the cost savings and passed it through to our customers.</li><li>We noticed our Scalers were still taking a bit longer to get through the images than we had expected. When we investigated, we realized they were spending a lot of time waiting for images to load. We spun up pre-fetching of the next image to speed things up. And again, we were able to take the cost savings and pass them through to our customers.</li></ul>There are countless more things we did to improve the Scale experience that our now available to all of our customers. And we promise to keep improving it. At Scale, we want to make our customers more productive and efficient. AI and self-driving cars are an awesome application of Scale towards that goal. If you need help with training your computer vision for self-driving cars,&nbsp;<a href="https://chat.scale.ai/" rel="noopener noreferrer" target="_blank">join our Slack</a>&nbsp;and ask. We’re confident we can deliver the cost, quality and Scale (pun intended) you need!

Training Data for Self Driving Cars - Computer Vision for SDC

Training Data For Self Driving Cars With Scale

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Blog

Guest Post: Cloudinary Uses Scale to Focus on Image Compression

Announcing Our Series A With Accel

Training Data For Self Driving Cars With Scale

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