Leilei Huang

Chenlong He, Leilei Huang, Wei Li et al.

Existing mezzanine image codecs lack specialized screen content coding tools and therefore struggle to maintain high image quality under bandwidth constraints, especially in areas with dense text. Although distribution codecs offer advanced screen content compression techniques, their high computational complexity makes them impractical for mezzanine coding. To address this shortfall, we introduce the High-quality Lightweight Codec (HLC), a solution centered on enabling practical, high-throughput palette for mezzanine coding. The core innovation is a novel data-dependency-free palette that eliminates the throughput bottlenecks. To ensure its effectiveness across all content, a co-designed rate-distortion optimization module arbitrates between the palette and traditional prediction modes, while a data reuse strategy between rate estimation and entropy coding minimizes the overall hardware resources required for the system. Experimental results show that, compared with a 4K@120fps JPEG-XS encoder, HLC achieves the same throughput while using only half the LUT resources and delivers BD-PSNR improvements of 3.461dB, 3.299dB, and 5.312dB on gaming, natural, and text content datasets, respectively.

Chenlong He, Zhijian Hao, Leilei Huang et al.

Just Noticeable Distortion (JND)-guided pre-filter is a promising technique for improving the perceptual compression efficiency of image coding. However, existing methods are often computationally expensive, and the field lacks standardized benchmarks for fair comparison. To address these challenges, this paper introduces a twofold contribution. First, we develop and open-source FJNDF-Pytorch, a unified benchmark for frequency-domain JND-Guided pre-filters. Second, leveraging this platform, we propose a complete learning framework for a novel, lightweight Convolutional Neural Network (CNN). Experimental results demonstrate that our proposed method achieves state-of-the-art compression efficiency, consistently outperforming competitors across multiple datasets and encoders. In terms of computational cost, our model is exceptionally lightweight, requiring only 7.15 GFLOPs to process a 1080p image, which is merely 14.1% of the cost of recent lightweight network. Our work presents a robust, state-of-the-art solution that excels in both performance and efficiency, supported by a reproducible research platform. The open-source implementation is available at https://github.com/viplab-fudan/FJNDF-Pytorch.