Abstract
In this work, we present and evaluate a hardware architecture for the LOCO-ANS (Low Complexity Lossless Compression with Asymmetric Numeral Systems) lossless and near-lossless image compressor, which is based on JPEG-LS standard. The design is implemented in two FPGA generations, evaluating its performance for different codec configurations. The tests show that the design is capable of up to 40.5 MPixels/s and 124 MPixels/s per lane for Zynq 7020 and UltraScale+ FPGAs, respectively. Compared to the single thread LOCO-ANS software implementation running in a 1.2 GHz Raspberry Pi 3B, each hardware lane achieves 6.5 times higher throughput, even when implemented in an older and cost-optimized chip like the Zynq 7020. Results are also presented for a lossless only version, which achieves a lower footprint and approximately 50% higher performance than the version that supports both lossless and near-lossless. Interestingly, these great results were obtained applying High-Level Synthesis, describing the coder with C++ code, which tends to establish a trade-off between design time and quality of results. These results show that the algorithm is very suitable for hardware implementation. Moreover, the implemented system is faster and achieves higher compression than the best previously available near-lossless JPEG-LS hardware implementation.
Highlights
Information compressors allow the reduction of bandwidth requirements and, given that data transmission systems tend to demand much more power than computing systems, they are useful as well when energy or dissipation is limited
In order to conduct the hardware verification, the system depicted in Figure 9 was implemented in two different Xilinx FPGA technologies, described in Table 1: Zynq 7
In the case of the high-frequency clock domain, the slowest paths of these implementations tend to be in the Two-Sided Geometric (TSG) coder and the output DMA for the Zynq 7020 implementation
Summary
Information compressors allow the reduction of bandwidth requirements and, given that data transmission systems tend to demand much more power than computing systems, they are useful as well when energy or dissipation is limited. A convenient way to perform this is to use near-lossless compression, which ensures that these errors are bounded by a limit set by the user. When this limit is set to zero, lossless compression is obtained. These codecs are useful when the data to compress contains very valuable information and/or, given the nature of the application, a minimum quality must be ensured. ANS coding system [26], to the arithmetic coder, codes a stream of symbols in a single output bitstream, where whole bits cannot be assigned to a particular input symbol That is, it codes the alphabet extension of order n = number of symbols. To be able to decode the resulting bitstream, the last ANS coder state must be sent to the decoder
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