  Optimized for pixel fidelity metrics, images compressed by existing image
codec are facing systematic challenges when used for visual analysis tasks,
especially under low-bitrate coding. This paper proposes a visual
analysis-motivated rate-distortion model for Versatile Video Coding (VVC) intra
compression. The proposed model has two major contributions, a novel rate
allocation strategy and a new distortion measurement model. We first propose
the region of interest for machine (ROIM) to evaluate the degree of importance
for each coding tree unit (CTU) in visual analysis. Then, a novel CTU-level bit
allocation model is proposed based on ROIM and the local texture
characteristics of each CTU. After an in-depth analysis of multiple distortion
models, a visual analysis friendly distortion criteria is subsequently proposed
by extracting deep feature of each coding unit (CU). To alleviate the problem
of lacking spatial context information when calculating the distortion of each
CU, we finally propose a multi-scale feature distortion (MSFD) metric using
different neighboring pixels by weighting the extracted deep features in each
scale. Extensive experimental results show that the proposed scheme could
achieve up to 28.17\% bitrate saving under the same analysis performance among
several typical visual analysis tasks such as image classification, object
detection, and semantic segmentation.
