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HLC: A High-Quality Lightweight Mezzanine Codec Featuring High-Throughput Palette

Chenlong He, Leilei Huang, Wei Li, Hanyang Cui, Zhijian Hao, Xiaoyang Zeng, Yibo Fan

Abstract

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.

HLC: A High-Quality Lightweight Mezzanine Codec Featuring High-Throughput Palette

Abstract

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.

Paper Structure

This paper contains 18 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Architectural and Algorithmic Co-Design
  3. High-Throughput PLT via a Dependency-Free Architecture
  4. The Data Dependency Bottleneck in Pixel Clustering
  5. A Virtual Cluster Table for Dependency-Free Clustering
  6. Compressing Cluster Indices via Run-Length Mapping
  7. Effective PLT Integration via a Co-Designed RDO
  8. Rate-Distortion Cost Estimation
  9. QP-lambda Table Tuning
  10. Hardware Cost Reduction via RDO-to-EC Data Reuse
  11. Miscellaneous
  12. Experiments
  13. Experiment Setup
  14. Comparison and Discussion
  15. Comparison with mezzanine codec
  16. ...and 3 more sections

Figures (4)

  • Figure 1: Hardware architecture of HLC, which contains three pipeline stages.
  • Figure 2: Hardware architecture and pipeline space-time diagram of pixel clustering engine (PCE). (a) Architecture. (b) Space-time diagram.
  • Figure 3: An illustration of run-length index mapping. (a) Pixels of CU represented by corresponding cluster index. (b) Mapping results.
  • Figure 4: Fitting results of the $R$-$D$ curve and how to get $QP-\lambda$ table.