A Quad Edge-Based Grid Encoding Model for Content-Aware Image Retargeting

Abstract

In this paper, we present a novel grid encoding model for content-aware image retargeting. In contrast to previous approaches such as vertex-based and axis-aligned grid encoding models, our approach takes each horizontal/vertical distance between two adjacent vertices as an optimization variable. Upon this difference-based encoding scheme, every vertex position of a target grid is subsequently determined after optimizing the one-dimensional values. Our quad edge-based grid model has two major advantages for image retargeting. First, the model enables a grid optimization problem to be developed in a simple quadratic program while ensuring the global convexity of objective functions. Second, due to the independency of variables, spatial regularizations can be applied in a locally adaptive manner to preserve structural components. Based on this model, we propose three quadratic objective functions. Note that, in our work, their linear combination guides a grid deformation process to obtain a visually comfortable retargeting result by preserving salient regions and structural components of an input image. Comparative evaluations have been conducted with ten existing state-of-the-art image retargeting methods, and the results show that our method built upon the quad edge-based model consistently outperforms other previous methods both on qualitative and quantitative perspectives.