Representing Images in 200 Bytes: Compression via Triangulation

TL;DR

This paper introduces a novel image compression method using adaptive triangulation and a new encoding algorithm, achieving better quality at extremely small sizes (~200 bytes) compared to JPEG and WebP.

Contribution

It presents a new triangulation-based image compression technique and a specialized encoding algorithm optimized for ultra-small images, outperforming existing codecs.

Findings

Achieves higher PSNR and SSIM than JPEG and WebP at 200 bytes.

Uses adaptive triangulation focusing on high entropy regions.

Provides a novel encoding algorithm for triangulated images.

Abstract

A rapidly increasing portion of internet traffic is dominated by requests from mobile devices with limited and metered bandwidth constraints. To satisfy these requests, it has become standard practice for websites to transmit small and extremely compressed image previews as part of the initial page load process to improve responsiveness. Increasing thumbnail compression beyond the capabilities of existing codecs is therefore an active research direction. In this work, we concentrate on extreme compression rates, where the size of the image is typically 200 bytes or less. First, we propose a novel approach for image compression that, unlike commonly used methods, does not rely on block-based statistics. We use an approach based on an adaptive triangulation of the target image, devoting more triangles to high entropy regions of the image. Second, we present a novel algorithm for encoding the triangles. The results show favorable statistics, in terms of PSNR and SSIM, over both the JPEG and the WebP standards.