This study addresses the challenge of domain adaptation in learned image compression, focusing on shifting the model from natural images to user-generated content (UGC) domain. We propose a novel entropy adapter framework augmented with knowledge distillation techniques to improve performance. Unlike existing adapter-based methods that primarily enhance transformation modules, we identify the mismatch between the adapter-based transformation and the fixed entropy network. To resolve this, we introduce adapters within the hypernet and entropy model. Specifically, our decoupled entropy adapter features a deeper residual structure with two independent branches, enabling a separate refinement of mean and scale components. This design improves the accuracy of probability estimation and overall compression efficiency. To further enhance the effectiveness of the adapters, we incorporate a knowledge distillation (KD) strategy with a progressive loss function. It facilitates a smooth transition from KD loss to a rate-distortion (RD) loss in the training process, effectively transferring knowledge from a directly fine-tuned model to the student model. Consequently, this strengthens the adapter's learning capability and improves compression performance. Experimental results show that the proposed method achieves a significant 11.5% bitrate savings compared to the baseline model. Additionally, it demonstrates robust adaptability across diverse network architectures.

In this paper we propose an alternative decoding method for inverse wavelet transform when only partial coefficients are available. We have been inspired by the recently developed compressive sensing (CS) decoding, which is capable in recovering sparse signals from a few linear and non-adaptive measurements. Let x be a sparse signal with N entries and only K out of them are non-zero, and y be its approximation coefficients. Classic CS decoding such as l1-minimization can be applied to decode x from y, and it indeed provides better reconstruction of sparse signals than direct inverse transform, as demonstrated by our simulation results. When coefficients have been quantized, the performance of CS decoding decreases more severely compared with direct inverse transform, but still better than the latter once the signal is sparse enough.

We investigate the usage of edge-based inpainting as an intra prediction method in block-based image compression. The joint utilization of edge information and the well-known Laplace equation yields a simple and effective inpainting algorithm. As for intra prediction, the edge-based inpainting is a uniform solution, yet adaptive to local image features. During the integration of edge-based inpainting into a block-based coding scheme, edge extraction and coding are jointly considered to achieve the rate-distortion optimization. Our proposed schemes are compared with JPEG2000, and experimental results demonstrate that both PSNR gain and visible quality improvement are achieved.

This paper proposes a new image compression scheme by introducing visual patterns to nonlinear interpolative vector quantization (IVQ). Input images are first distorted by a generic down-sampling so that some details are removed before compression. Then, the distorted images are compressed lossly by traditional image coding scheme and transmitted to the decoder. In the decoder side, VQ indices are extracted from the decoded images to reproduce the removed details from a pre-trained codebook. One of main contributions in this paper is, we introduce visual patterns on designing the codebook, where only removed details that contain visual patterns and their original counterparts as pairs are trained. Experimental results show: (1) visual pattern blocks are easy to form clusters than original blocks; (2) the proposed scheme achieves much better performance over JPEG in terms of visual quality and PSNR.