Name: | Description: | Size: | Format: | |
---|---|---|---|---|
7.8 MB | Adobe PDF |
Abstract(s)
This work presents a novel approach to the detection of mitochondria in microscopy
images within the compressed domain, eliminating the need for full image decompression
while maintaining high detection accuracy. The proposed architecture,
Processing of Microscopy Images in the Compressed Domain (ProMIC), integrates
a Domain Translator architecture, enabling object detection networks to operate
directly on images’ latent representations. This approach significantly reduces computational
costs while preserving essential visual information for detection tasks.
The study also included a preliminary analysis of JPEG-AI compression efficiency
against classical codecs (HEVC/H.265 and VVC/H.266) across multiple datasets.
The proposed method builds upon a Base model inspired by a previous compresseddomain
object detection approach. Key innovations in this work include: the use
of a Lightweight Residual Block that enhances feature extraction from latent
representations, improving detection robustness across different compression rates;
a Pixel Domain Fine-Tuning process that enhances the Domain Adaptation of the
model; and a Guided Domain Translator Training strategy that refines the Domain
Translator to extract meaningful information directly from latent codes, minimising
the gap between compressed and pixel-domain object detection models.
Extensive experiments were conducted to compare the performance of both the
Base model and the proposed architecture against a reference object detection
model that operates on reconstructed images. Results on the Lucchi++ dataset
show that ProMIC significantly outperforms the Base model. Notably, the proposed
architecture exceeds the reference model in mean average precision, achieving a 2.31
percentage point improvement at the highest bitrate. This highlights the potential
of compressed-domain image processing to capture meaningful features that may
be lost in fully reconstructed images. Additionally, applying the ProMIC method
reduced the complexity of the object detection system by 42.34% compared to the
reference model.
These findings validate the feasibility of direct object detection in latent codes, offering
a computationally efficient alternative to traditional pixel-domain approaches.
The proposed framework has promising applications in real-time processing, embedded
systems, and large-scale AI-driven image analysis, paving the way for more
efficient and scalable vision models that operate directly in the compressed domain.
Description
Keywords
Engenharia eletrónica Imagem Computação Imagem por computação Imagem de Microscopia no domínio comprimido