Streaming Learning Center Where Streaming Professionals Learn to Excel
Related Articles
If you’ve been following my work, you know that I’m very bullish on per-title encoding and optimization technologies, and have reviewed them several times. One technology that I haven’t tested before is from Crunch Media Works, which offers video optimization tools that can be used on public and private computer servers, as well as mobile devices, to reduce the bandwidth of videos prior to uploading or delivery.
Briefly, I tested Crunch’s technology against other technologies that I’ve worked with in the past and others that I could access via SaaS applications on the Internet. I documented my findings in a report entitled, Per-Title Encoding Comparison: Crunch Video Optimization Technology compared to Brightcove CAE, Capped CRF, Capella Systems SABL, JWPlayer, and Mux Video, which you can download below.
Here’s my conclusion from the report, and you can see the summary table above. “As you can see, Crunch delivered substantial savings compared to all other technologies while remaining visually indistinguishable as measured by SSIM scores with anecdotal subjective verification. The ability to tune the Crunch algorithm for a specific SSIM result allows publishers to achieve their own targeted quality/bandwidth tradeoff. This is another strength of the Crunch technology.”
Crunch tools are available as a component of a software-based or cloud encoder. Crunch can also be added as an SDK to other developers’ applications. If you’re interested in learning more about Crunch’s technology, please contact sales@crunchmediaworks.com.
Here’s a link to download the report.
I am Rishika Carpenter. I’m a Computer Science grad from NIT Surathkal, and right now, I’m working as an R&D Engineer at Synopsys. I’ve always been into solving complex problems—whether it’s digging into memory leaks and CLI tools at my current role or building out full systems from scratch .
I’ve had some pretty cool experiences along the way. I interned at Microsoft, where I built a scoring algorithm for user behavior and managed to bump their test coverage all the way from 60% to 95%. I’ve also worked on the startup side at places like Slide and Deep Cognition, where I did everything from setting up robust OTP and push notification systems to automating feedback loops for LLMs .
Tech-wise, I’m pretty versatile. I’m comfortable with C++, Python, and Java, and I’ve spent a lot of time with frameworks like Spring Boot, Django, and React . I also love diving into research—like my project on Federated Learning, where I worked on making Industrial IoT more secure. Basically, I just love building stuff that actually works and scales.