Reduced Bit Median Quantization (RBMQ) is a novel image compression technique that combines median quantization and bit reduction to achieve substantial file size reduction while maintaining acceptable image quality, making it suitable for both general use and deep archival storage.
PDE 기반 이미지 압축에서 adjoint 방법을 이용하여 최적의 픽셀 집합을 선택하는 방법을 제안한다. 이를 통해 노이즈가 있는 이미지에서 누락된 영역을 효과적으로 복원할 수 있다.
The proposed method leverages a privileged end-to-end decoder to correct the score function of a diffusion model, achieving better perceptual quality while guaranteeing distortion.
Utilizing theoretical bounds to guide neural image codecs improves performance significantly.
Neural image compression is improved by the Quantization Rectifier method, preserving feature expressiveness for better image quality.
Learned Image Compression with ROI-Weighted Distortion and Bit Allocation enhances perceptual quality through region-based bit allocation.
Non-isotropic diffusion model and innovative entropy model improve image compression quality.
Enhancing image compression through a conditional diffusion-based decoder and an innovative entropy model.
提案された周波数感知トランスフォーマー(FAT)ブロックは、LICにおける多様な周波数情報のモデリングに挑戦し、状態-of-the-artのレート-歪み性能を達成します。
Combining MSE-based models and generative models using Hierarchical-ROI and adaptive quantization improves visual quality at low bit rates.