Qi Zhang

15 papers A* 1B 2C 2Journal 10
YearRankTypeTitle / Venue / Authors
2025 J jnl
End-to-End RGB-IR Joint Image Compression With Channel-wise Cross-modality Entropy Model.
CoRR
Haofeng Wang, Fangtao Zhou, Qi Zhang, Zeyuan Chen, Enci Zhang, Zhao Wang, Xiaofeng Huang, Siwei Ma
2025 A* conf
HGC-Avatar: Hierarchical Gaussian Compression for Streamable Dynamic 3D Avatars.
ACM Multimedia
Haocheng Tang, Ruoke Yan, Xinhui Yin, Qi Zhang, Xinfeng Zhang, Siwei Ma, Wen Gao, Chuanmin Jia
2025 J jnl
HGC-Avatar: Hierarchical Gaussian Compression for Streamable Dynamic 3D Avatars.
CoRR
Haocheng Tang, Ruoke Yan, Xinhui Yin, Qi Zhang, Xinfeng Zhang, Siwei Ma, Wen Gao, Chuanmin Jia
2025 B conf
LL-ICM: Image Compression for Low-Level Machine Vision via Large Vision-Language Model.
DCC
Yuan Xue, Qi Zhang, Chuanmin Jia, Shiqi Wang
2025 B conf
Point Cloud-Assisted Neural Image Compression.
DCC
Ziqun Li, Qi Zhang, Xiaofeng Huang, Zhao Wang, Siwei Ma, Wei Yan
2024 J jnl
LL-ICM: Image Compression for Low-level Machine Vision via Large Vision-Language Model.
CoRR
Yuan Xue, Qi Zhang, Chuanmin Jia, Shiqi Wang
2024 C conf
Low-Complexity 3D-Vision Conferencing System based on Accelerated RIFE Model.
PCS
Hongyue Huang, Xilong Zhou, Hongbo Ning, Haopeng Lu, Qi Zhang, Yanpeng Liang, Wanjun Lyu, Chuanmin Jia, Xinfeng Zhang, Liuxin Zhang, Siwei Ma
2024 J jnl
Perceptual Video Coding for Machines via Satisfied Machine Ratio Modeling.
IEEE Trans. Pattern Anal. Mach. Intell.
Qi Zhang, Shanshe Wang, Xinfeng Zhang, Chuanmin Jia, Zhao Wang, Siwei Ma, Wen Gao
2024 J jnl
Point Cloud-Assisted Neural Image Compression.
CoRR
Ziqun Li, Qi Zhang, Xiaofeng Huang, Zhao Wang, Siwei Ma, Wei Yan
2024 J jnl
Pose-Driven Compression for Dynamic 3D Human via Human Prior Models.
IEEE Trans. Pattern Anal. Mach. Intell.
Ruoke Yan, Qian Yin, Xinfeng Zhang, Qi Zhang, Gai Zhang, Siwei Ma
2024 J jnl
Predicting Satisfied User and Machine Ratio for Compressed Images: A Unified Approach.
CoRR
Qi Zhang, Shanshe Wang, Xinfeng Zhang, Siwei Ma, Jingshan Pan, Wen Gao
2023 J jnl
RealVR: Efficient, Economical, and Quality-of- Experience-Driven VR Video System Based on MPEG OMAF.
IEEE Trans. Multim.
Qi Zhang, Jianchao Wei, Shanshe Wang, Siwei Ma, Wen Gao
2022 J jnl
SMR: Satisfied Machine Ratio Modeling for Machine Recognition-Oriented Image and Video Compression.
CoRR
Qi Zhang, Shanshe Wang, Xinfeng Zhang, Chuanmin Jia, Jingshan Pan, Siwei Ma, Wen Gao
2021 J jnl
Just Recognizable Distortion for Machine Vision Oriented Image and Video Coding.
Int. J. Comput. Vis.
Qi Zhang, Shanshe Wang, Xinfeng Zhang, Siwei Ma, Wen Gao
2020 C conf
A Novel Visual Analysis Oriented Rate Control Scheme for HEVC.
VCIP
Qi Zhang, Shanshe Wang, Siwei Ma

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

In 2025, he co-authored Assessing the Effectiveness of the Tigress Obfuscator Against MOPSA and BinaryNinja, published at CheckMATE. The paper evaluates the effectiveness of obfuscation techniques and their impact on source-level static analysis and binary-level decompilation.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.