Memory-efficient hardware architecture of 2-D dual-mode lifting-based discrete wavelet transform

[[abstract]]Memory requirements (for storing intermediate signals) and critical path are the essential issues for two-dimensional (2-D) (or multi-dimensional) transforms. This work presents new algorithms and hardware architectures to address the above issues in 2-D dual-mode (supporting 5/3 lossless and 9/7 lossy coding) Lifting-based Discrete Wavelet Transform (LDWT). The proposed 2-D dual-mode LDWT architecture has the merits of low-transpose memory, low latency, and regular signal flow, making it suited for VLSI implementation. The transpose memory requirement of the N×N 2-D 5/3 mode LDWT and 2-D 9/7 mode LDWT are 2N and 4N, respectively. Comparison results indicate that the proposed hardware architecture has a lower lifting-based low-transpose memory size requirement than the previous architectures. As a result, it can be applied to real-time visual operations such as JPEG2000, Motion-JPEG2000, MPEG-4 still texture object decoding, and wavelet-based scalable video coding (SVC) applications.[[incitationindex]]SCI[[booktype]]紙