An FPGA-Based LOCO-ANS Implementation for Lossless and Near-Lossless Image Compression Using High-Level Synthesis

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.

Color Image Compressor using Discrete Wavelet Transform and Block Based Image Coding

In the present time of sight and sound, the necessity of picture/video stockpiling and video playback, transmission for video conferencing, picture and video recovery and so forth are expanding exponentially. Thus, the need of better pressure innovation is consistently popular. Present day applications, notwithstanding high pressure proportion, additionally interest for proficient encoding and deciphering forms, with the goal that computational imperative of some continuous applications is fulfilled. Two broadly utilized spatial space pressure systems are discrete wavelet transform and multilevel block truncation coding (BTC). DWT method is used to stationary and non-stationary images and applied to all average pixel value of image. Muli-level BTC is a kind of lossy picture pressure strategy for greyscale pictures. In this,the unique picture is partitioned into squares and after that uses a quantizer to decrease the quantity of dark levels in each square while keeping up a similar mean and standard deviation. In this paper is studied of Multi-level BTC and DWT technique for gray and color image.