An FPGA Implementation of Displacement Vector Search for Intra Pattern Copy in JPEG XS
By Qiyue Chen, Yao Li, Jie Tao, Song Chen, Li Li, Dong Liu
University of Science and Technology of China, Hefei, China

Abstract
Recently, progress has been made on the Intra Pattern Copy (IPC) tool for JPEG XS, an image compression standard designed for low-latency and low-complexity coding. IPC performs wavelet-domain intra compensation predictions to reduce spatial redundancy in screen content. A key module of IPC is the displacement vector (DV) search, which aims to solve the optimal prediction reference offset. However, the DV search process is computationally intensive, posing challenges for practical hardware deployment. In this paper, we propose an efficient pipelined FPGA architecture design for the DV search module to promote the practical deployment of IPC. Optimized memory organization, which leverages the IPC computational characteristics and data inherent reuse patterns, is further introduced to enhance the performance. Experimental results show that our proposed architecture achieves a throughput of 38.3 Mpixels/s with a power consumption of 277 mW, demonstrating its feasibility for practical hardware implementation in IPC and other predictive coding tools, and providing a promising foundation for ASIC deployment.
To read the full article, click here
Related Semiconductor IP
- JPEG XS - Low-Latency Video
- JPEG XS Decoder (FPGA) - Performance-oriented version
- JPEG XS Decoder (FPGA) - Standard version
- JPEG XS Encoder (FPGA) - Performance-oriented version
- JPEG XS Encoder (FPGA) - Standard version
Related Articles
- An 800 Mpixels/s, ~260 LUTs Implementation of the QOI Lossless Image Compression Algorithm and its Improvement through Hilbert Scanning
- The rise of FPGA technology in High-Performance Computing
- Design and implementation of a hardened cryptographic coprocessor for a RISC-V 128-bit core
- Hardware vs. Software Implementation of Warp-Level Features in Vortex RISC-V GPU
Latest Articles
- ZK-Flex: A Flexible and Scalable Framework for Accelerating Zero-Knowledge Proofs
- ITP-STDP: An Intrinsic-Timing Power-of-Two Learning Engine for On-Chip SNN Training
- OpenEye: A Scalable Open-Source Hardware Accelerator for DNNs
- CHIMERA: A Flexible and Scalable 3.1 TOPS/W AI-MCU with Transformer Accelerator and 563 Gb/s Shared-L2 Memory Subsystem with QoS Guarantees
- CXL-ClusterSim: Modeling CXL-based Disaggregated Memory Cluster for Pooling and Sharing using gem5 and SST