Enhanced Quality Aware-Scalable Underwater Image Compression
🤖 AI Summary
Underwater image transmission faces dual challenges of limited bandwidth and severe distortion, making it difficult for existing methods to simultaneously achieve efficient compression and quality enhancement. This paper proposes a quality-aware scalable coding framework that implicitly integrates enhancement capabilities into the compression process through collaborative design of base and enhancement layers. Key contributions include: (1) an enhancement dictionary with shared sparse coefficients; (2) a dual-branch filtering architecture—comprising coarse filtering followed by detail refinement; and (3) joint residual redundancy removal and controllable non-zero coefficient coding. Extensive experiments on five large-scale underwater datasets demonstrate significant improvements in the Underwater Image Quality Measure (UIQM), achieving a superior trade-off between compression ratio and visual fidelity. The proposed method consistently outperforms state-of-the-art approaches across quantitative metrics and qualitative assessments.
Application Category
📝 Abstract
Underwater imaging plays a pivotal role in marine exploration and ecological monitoring. However, it faces significant challenges of limited transmission bandwidth and severe distortion in the aquatic environment. In this work, to achieve the target of both underwater image compression and enhancement simultaneously, an enhanced quality-aware scalable underwater image compression framework is presented, which comprises a Base Layer (BL) and an Enhancement Layer (EL). In the BL, the underwater image is represented by controllable number of non-zero sparse coefficients for coding bits saving. Furthermore, the underwater image enhancement dictionary is derived with shared sparse coefficients to make reconstruction close to the enhanced version. In the EL, a dual-branch filter comprising rough filtering and detail refinement branches is designed to produce a pseudo-enhanced version for residual redundancy removal and to improve the quality of final reconstruction. Extensive experimental results demonstrate that the proposed scheme outperforms the state-of-the-art works under five large-scale underwater image datasets in terms of Underwater Image Quality Measure (UIQM).
Problem
Research questions and friction points this paper is trying to address.
Compresses underwater images while enhancing quality
Addresses limited bandwidth and distortion in aquatic environments
Uses scalable framework with sparse coding for efficiency
Innovation
Methods, ideas, or system contributions that make the work stand out.
Scalable compression with base and enhancement layers
Shared sparse coefficients for compression and enhancement
Dual-branch filtering for redundancy removal and quality improvement
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