A High Throughput and Energy Efficient Lepton Hardware Encoder With Hash-Based Memory Optimization

Although it has been surpassed by many subsequent coding standards, JPEG occupies a large storage share of the current data hosting service. To reduce the storage costs, DropBox proposed a secondary lossless compression algorithm, Lepton, to further improve the compression rate of JPEG images. However, the bloated probability models defined by Lepton severely restrict its throughput and energy efficiency. To solve this problem, we construct an access probability-based hash function for the probability models, and then propose a hardware-friendly memory optimization method by combining the proposed hash function with N-way Set-Associative unit. Besides, we also propose a synchronization mechanism for the serial accessing probability model, so that the syntax elements can be processed in parallel without changing the resulting bitstream. After that, we implement a high throughput, high energy efficiency, and low-cost Lepton hardware encoder. To the best of our knowledge, this is the first hardware implementation of transparent image recompression. The synthesis result shows that the proposed hardware structure reduces the total area of the probability models by 70.97%. Compared with DropBox’s software solution, the throughput and the energy efficiency of the proposed Lepton hardware encoder are increased by 63 and 1398 times on average. In terms of manufacturing cost, the proposed Lepton hardware encoder is also lower than the general-purpose CPU used by DropBox.