CMWMF: Constant Memory Architecture of Weighted Mode/Median Filter for Extremely Large Label Depth Refinement
Journal
IEEE Transactions on Circuits and Systems for Video Technology
Journal Volume
31
Journal Issue
8
Pages
2981-2993
Date Issued
2021
Author(s)
Wu S.-S
Abstract
In this study, we propose a constant memory hardware architecture that can support weighted mode, median, and joint bilateral filters, which is referred to as CMWMF. This work aims to meet the high memory and computation requirements of processing depth maps with a large number of depth candidates. In the proposed architecture, we leverage the geometry smoothing characteristic of natural images to reduce the static random access memory (SRAM) size for hardware implementation. The architecture preserves a constant number of disparity values instead of depending on the label count and size of the local supporting window. A novel weighted median search procedure is proposed, which assigns a computation to each input cycle, thereby rendering the process hardware friendly. An index-checking technique is proposed to process out-of-order joint histograms. We adopted the above-mentioned techniques in our architecture as they consume a constant SRAM size and supports multiple types of filters. As a result, this architecture reduces the SRAM size by 92.4% with a negligible decrease in performance. According to our analysis on the KITTI, and Middlebury datasets, and with actual depth cameras, the preserved information is sufficient. The proposed architecture is one of the most suitable depth refinement architectures for scenarios having a large number of depth candidates. ? 1991-2012 IEEE.
Subjects
Depth enhancement
Memory efficiency
VLSI architecture design
Weight median filter
Weighted mode filter
Median filters
Memory architecture
Bilateral filters
Constant memory
Hardware architecture
Hardware implementations
Joint histograms
Proposed architectures
Search procedures
Static random access memory
Static random access storage
Type
journal article