# A Structured Approach to Predicting Image Enhancement Parameters

## TL;DR

This paper introduces a novel matrix factorization-based method to predict personalized image enhancement parameters directly from image features, eliminating the need for training images and improving efficiency and accuracy.

## Contribution

It proposes a new structured approach using matrix factorization to predict enhancement parameters solely from image features, advancing beyond heuristic and existing MF methods.

## Key findings

- Outperforms heuristic methods in accuracy
- Outperforms recent matrix factorization approaches
- Effective on both synthetic and real-world data

## Abstract

Social networking on mobile devices has become a commonplace of everyday life. In addition, photo capturing process has become trivial due to the advances in mobile imaging. Hence people capture a lot of photos everyday and they want them to be visually-attractive. This has given rise to automated, one-touch enhancement tools. However, the inability of those tools to provide personalized and content-adaptive enhancement has paved way for machine-learned methods to do the same. The existing typical machine-learned methods heuristically (e.g. kNN-search) predict the enhancement parameters for a new image by relating the image to a set of similar training images. These heuristic methods need constant interaction with the training images which makes the parameter prediction sub-optimal and computationally expensive at test time which is undesired. This paper presents a novel approach to predicting the enhancement parameters given a new image using only its features, without using any training images. We propose to model the interaction between the image features and its corresponding enhancement parameters using the matrix factorization (MF) principles. We also propose a way to integrate the image features in the MF formulation. We show that our approach outperforms heuristic approaches as well as recent approaches in MF and structured prediction on synthetic as well as real-world data of image enhancement.