Despite significant advances in image compression, existing evaluation metrics remain poorly aligned with human visual perception-particularly under extremely low bitrates, where reconstructed images often suffer from abstract distortions or semantic degradation that are difficult for conventional metrics to capture. To address this limitation, we propose MG-VLQA, a novel multi-granularity quality assessment framework that leverages VisionLanguage Models (VLMs) to evaluate image reconstruction fidelity through the lens of semantic consistency with the original caption. Our method formulates a suite of captionderived questions spanning three complementary dimensions: (1) entity presence (semantic completeness), (2) detail fidelity (local appearance accuracy), and (3) inter-entity interactions (relational coherence). By simulating human-like perceptual judgment via VLMbased question answering and semantic similarity scoring, MG-VLQA provides a more interpretable, fine-grained, and perceptually relevant assessment of compression quality. Extensive experiments across multiple datasets and codecs demonstrate that our metric achieves higher correlation with human judgment and offers superior discriminative power.

Speech coding serves as a means of data compression, aiming to decrease the expenses related to data storage and transmission. The efficacy of compressing speech efficiently through neural networks has been demonstrated in methods using vector quantization (VQ). However, the complex procedure of VQ makes it challenging to fit into frameworks and limits compression at discrete bitrate points. This paper proposes a neural speech compression framework, which achieves flexible bitrate speech reconstruction through compact latent representation and better entropy estimation.