Screen content has emerged as a prominent medium in our increasingly connected world. However, compressed screen content images often suffer from unpleasant artifacts, significantly obstructing the comprehension of text and graphic regions. In this paper, we introduce a quality enhancement framework specifically designed for compressed screen content images. We first propose a dataset for enhancing the quality of screen content images affected by various levels of compression distortion, using state-of-the-art Versatile Video Coding with screen content coding techniques enabled. Given the unique characteristics of screen content images, our enhancement framework incorporates B-spline representation to mitigate the quality degradation caused by compression. Additionally, we focus on recovering distorted text by detecting text regions within the degraded image and generating a pristine textual map to guide the recovery process. Experimental results demonstrate that our proposed method effectively enhances the quality of reconstructed screen content images across different compression distortion levels, leading to the quantitative and qualitative improvement.

Recent exploration efforts in JVET (Joint Video Experts Team of ITU-T SG 16 WP 3 and ISO/IEC JTC 1/SC29) has achieved progresses on neural network-based video coding (NNVC)11NNVC is also the name of the reference software for evaluating neural network-based video coding technologies in JVET. The project locates at https://vcgit.hhi.fraunhofer.de/jvet-ahg-nnvc/VVCSoftware_VTM. Latest version of NNVC features two normative deep tools, i.e., neural network-based intra prediction and neural network-based in-loop filtering. Specifically, the neural network-based filtering in NNVC supports three operating points, known as VLOP (very low-complexity operating point), LOP (low-complexity operating point), and HOP (high-complexity operating point). LOP filter receives more attention among these three due to its favorable performance-complexity trade-off. In this paper, we introduce CCLOP, a cross-component enhanced LOP filter. CCLOP builds upon LOP filter in NNVC but incorporates deep luma features for chroma filtering. We conduct extensive experiments to verify the effectiveness of CCLOP. Compared with NNVC-10, the latest reference software of NNVC, CCLOP achieves {-0.13%, −2.27%, −3.11%}, {-0.18%, −2.07%, −3.21%}, and {-0.03%, −1.81%, −2.51%} BD-rate changes on average for {Y, Cb, Cr} under random-access, low-delay, and all-intra configurations respectively, while maintaining the same complexity as existing LOP filter ([email protected] kMAC/pixel, [email protected] kMAC/pixel).

Neural compression methods often rely on highly expressive models to fit large datasets, resulting in significant decoding complexity. Overfitted codecs have been proposed as an alternative to reduce decoding complexity. However, these approaches typically lack flexibility in encoding configurations. To address this, we introduce CD, a neural video compression method that employs picture-wise overfitting. CD is built upon the Cool-chic video framework [1], but incorporates Decoupled representations for motion and residue. Additionally, we propose an effective training strategy for CD to further enhance its performance.

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, a hybrid video compression framework is proposed that serves as a demonstrative showcase of deep learning-based approaches extending beyond the confines of traditional coding methodologies. The proposed hybrid framework is founded upon the Enhanced Compression Model (ECM), which is a further enhancement of the Versatile Video Coding (VVC) standard. We have augmented the latest ECM reference software with well-designed coding techniques, including block partitioning, deep learning-based loop filter, and the activation of block importance mapping (BIM) which was integrated but previously inactive within ECM, further enhancing coding performance. We evaluate the coding performance of the proposed framework with extensive experiments on the JVET dataset compared with ECM10.0 and VTM-11.0. Due to the testing environment and the coding complexity of the ECM, we did not conduct testing on Class A. The QPs are set as 22, 27, 32, 37, and 42. Compared with ECM-10.0, our method achieves 6.26%, 13.33%, and 12.33% BD-rate savings for the Y, U, and V components under random access (RA) configuration. The traditional hybrid coding framework combined with the three coding tools can further improve compression efficiency and has great potential for performance improvement.