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Boost Video Quality Before & Save Costs

Perceptual preprocessing removes grain, artifacts, and wasted bits so encoders start with cleaner pixels and you ship sharper streams at lower bitrates.

TL;DR

• Pre-encode AI preprocessing (denoise, deinterlace, super-resolution, saliency masking) removes up to 60 % of visible noise and lets codecs spend bits only where they matter.

• Combined with H.264/HEVC, these filters deliver 25 – 35 % bitrate savings at equal-or-better VMAF, trimming multi-CDN bills without touching player apps.

• Viewers are ruthless: 33 % quit a stream for poor quality, jeopardising up to 25 % of OTT revenue.

• Sima Labs’ SimaBit plugs into codecs e.g. (x264, HEVC, SVT-AV1, etc..), runs in real time (< 16 ms / 1080p frame).

• Net result: sharper streams, lower churn, and 6-figure CDN savings -- all before the first GOP hits the encoder.

Introduction

• Viewers bounce when the picture stutters — 33 % quit a stream for poor quality, draining up to 25 % of annual OTT revenue (Bitmovin).

• Preprocessing tackles the junk before H.264, HEVC, or VVC ever see a frame, squeezing out hidden bandwidth savings.

• Sima Labs’ SimaBit engine slots into existing pipelines and pushes visibly higher-quality and more compressible frames through the same pipe — no player changes required.

• This guide lays out essentials: why it matters, key techniques, how SimaBit works, tool choices, best practices, and what’s next.

1. Why Preprocessing Became Mission-Critical

Exploding scale
Video traffic will hit 82 % of all IP traffic by mid-decade.
Live sports, UGC, training, and broadcast feeds all compete for the same CDN purse.

Codec limits
Codecs can’t efficiently distinguish between noise and perceptually important textures, thus wasting bits.

Viewer intolerance
According to Telestream, 86% of users expect TV-grade clarity on every device.
Even Netflix’s Tyson-Paul stream logged 90k quality complaints in a single night (Reuters).

Cost spiral
Every extra 500 kbps inflates multi-CDN bills; multiply that by millions of watch-hours.
AI prep slashes bitrate up to 30 %+ while keeping (or raising) Netflix's VMAF & other Video Quality Metrics.

2. Core AI-Powered Preprocessing Techniques

According to Project-Aeon, “AI tools analyze individual frames for pixelation, blur, or compression artifacts in real time” (Aeon).

3. Under the Hood: How SimaBit Super-Charges Codecs

• Codec-Side Intelligence

• Plug-ins for all video codecs (H.264, AV1, etc..)

• SimaBit passes a cleaned frame buffer so the codec’s rate-control bets bandwidth where eyeballs focus.

• Latency < 16 ms per 1080p frame—safe for LIVE.

• Bandwidth ROI Model

• In the frequency domain, SimaBit is able to identify the perceptaully relevant frequencies that codecs struggle with and fix them beforehand.

4. Tool Comparison - Where SimaBit Stands

• Codec-Side Intelligence

• Plug-ins for all video codecs (H.264, AV1, etc..)

• SimaBit passes a cleaned frame buffer so the codec’s rate-control bets bandwidth where eyeballs focus.

• Latency < 16 ms per 1080p frame—safe for LIVE.

• Bandwidth ROI Model

• In the frequency domain, SimaBit is able to identify the perceptaully relevant frequencies that codecs struggle with and fix them beforehand.

5. Best Practices for Dropping Preprocessing into Your Pipeline

Start with a Representative Sample

• Try SimaBit on the most difficult confetti, textured grass, large set of video and see the quality + bitrate improvement :)

QA Loop

• Feed frames to Prime-Video-style ML detectors—catch block corruption early (Amazon Science).

Conclusion

Better picture quality, less bits over the wire—that’s the preprocessing goal! SimaBit let you reclaim 25-35 % of bandwidth, slash CDN bills, and keep fickle viewers glued to the screen. Drop SimaBit into your transcode farm, fire up the saliency dashboard, and watch your VMAF curve climb while Mbps fall.