Mobile/Google

Google 신규 JPEG Encoder 'Guetzli' 공개

아브리얼 2017. 3. 18. 22:22



From : Google Research Blog

https://research.googleblog.com/2017/03/announcing-guetzli-new-open-source-jpeg.html


구글에서 기존 JPEG Encoder 대비 35% 압축률이 좋은 신규 Encoder 'Guetzli' 소스를 공개함

2014년 WebP 코덱을 발표하며 코덱개선에 많은 투자와 개발을 하고 있는 Google

하지만 WebP의 경우에는 Client Server 모두 지원이 되어야 하므로 시장보급에 걸려 많이 확대는 되지 못함

하지만 그럼에도 불구하고 이런 많은 코덱 개발을 하는 것은 결국 구글에게는 Money와 연결되는 문제이기 때문

Google 의 주 비즈니스는  Internet 기반이며 상당수의 데이터가 영상 데이터.

JPEG은 이러한 영상포맷의 시장표준이며 이를 보다 개선했을 때 구글의 서버 트래픽 및 스토리지의

비용절감으로 연결이 되므로 결코 일부러 자선사업하는 것은 아닌셈.


하지만 이러한 것에 오랫동안 투자와 개발을 계속 하는 것은 확실히 부러운 정책이며 환경임.

어찌되었든 사용자 입장에서 데이터 트랙픽 이용하는 비용이 절감될 뿐만 아니라

빠른 페이지 로딩을 기대할 수 있으므로 굿보고 떡먹는 셈?


포브스 분석에 따르며 2010년 평균 700KB의 이미지를 웹페이지에서 사용했다면

요즘은 약 2300KB 약 3배이상으로 증가하였기 때문에 이에 따른 페이지 로딩 시간 증가는 Cloud 서비스 업체의 숙제.




(Cross-posted on the Google Open Source Blog)

At Google, we care about giving users the best possible online experience, both through our own services and products and by contributing new tools and industry standards for use by the online community. That’s why we’re excited to announce Guetzlia new open source algorithm that creates high quality JPEG images with file sizes 35% smaller than currently available methods, enabling webmasters to create webpages that can load faster and use even less data.

Guetzli [guɛtsli] — cookie in Swiss German — is a JPEG encoder for digital images and web graphics that can enable faster online experiences by producing smaller JPEG files while still maintaining compatibility with existing browsers, image processing applications and the JPEG standard. From the practical viewpoint this is very similar to our Zopfli algorithm, which produces smaller PNG and gzip files without needing to introduce a new format, and different than the techniques used in RNN-based image compressionRAISR, and WebP, which all need client changes for compression gains at internet scale. 

The visual quality of JPEG images is directly correlated to its multi-stage compression process: color space transformdiscrete cosine transform, and quantization. Guetzli specifically targets the quantization stage in which the more visual quality loss is introduced, the smaller the resulting file. Guetzli strikes a balance between minimal loss and file size by employing a search algorithm that tries to overcome the difference between the psychovisual modeling of JPEG's format, and Guetzli’s psychovisual model, which approximates color perception and visual masking in a more thorough and detailed way than what is achievable by simpler color transforms and the discrete cosine transform. However, while Guetzli creates smaller image file sizes, the tradeoff is that these search algorithms take significantly longer to create compressed images than currently available methods.
Figure 1. 16x16 pixel synthetic example of a phone line hanging against a blue sky — traditionally a case where JPEG compression algorithms suffer from artifacts. Uncompressed original is on the left. Guetzli (on the right) shows less ringing artefacts than libjpeg (middle) and has a smaller file size.
And while Guetzli produces smaller image file sizes without sacrificing quality, we additionally found that in experiments where compressed image file sizes are kept constant that human raters consistently preferred the images Guetzli produced over libjpeg images, even when the libjpeg files were the same size or even slightly larger. We think this makes the slower compression a worthy tradeoff.
Figure 2. 20x24 pixel zoomed areas from a picture of a cat’s eye. Uncompressed original on the left. Guetzli (on the right) 
shows less ringing artefacts than libjpeg (middle) without requiring a larger file size.
It is our hope that webmasters and graphic designers will find Guetzli useful and apply it to their photographic content, making users’ experience smoother on image-heavy websites in addition to reducing load times and bandwidth costs for mobile users. Last, we hope that the new explicitly psychovisual approach in Guetzli will inspire further image and video compression research.