🤖 AI Summary
This work addresses the limitations of conventional matching pursuit–based image compression, which employs fixed-size blocks and struggles to balance efficiency and reconstruction quality across both complex and smooth image regions. To overcome this, the authors propose a novel approach that integrates adaptive quadtree partitioning with matching pursuit, dynamically adjusting block sizes according to local image structure. Furthermore, they introduce a multi-objective Bayesian optimization framework based on a tree-structured Parzen estimator (TPE) to achieve an optimal trade-off between compression ratio and structural similarity index (SSIM). The method substantially enhances compression performance, attaining up to four times the compression ratio of JPEG at comparable SSIM levels, while also enabling multi-level parallelization for applicability across diverse compression scenarios.
📝 Abstract
We present AQMP, a novel image codec combining Adaptive Quadtree Refinement with Matching Pursuit. Unlike conventional Matching Pursuit methods that operate on fixed-size sub-images, AQMP dynamically adapts block sizes to local image structure, allocating finer partitions where the image is complex and coarser ones where it is smooth. This adaptivity yields superior compression ratios compared to fixed-size block Matching Pursuit at equivalent image quality, while offering significant parallelization opportunities at both the tree-leaf level and during compression of individual nodes. The algorithm is governed by user-specified accuracy and sparsity parameters alongside a small set of additional hyperparameters. To navigate the trade-off between compression efficiency and visual quality, we perform multi-objective hyperparameter optimization using the Tree-Structured Parzen Estimator, producing comprehensive Pareto fronts. Experimental results show that AQMP achieves up to $4\times$ higher compression rates than JPEG at comparable SSIM values, while maintaining competitive quality across a broad range of compression regimes. Performance evaluation is provided using a representative set of test images. To ensure reproducibility and promote adoption, we have made our implementation publicly available on GitHub under the MIT license.