基于小波分析的圖像稀疏保真度評價
doi: 10.11999/JEIT150173
基金項目:
國家自然科學(xué)基金(60975008)和重慶市教委科學(xué)技術(shù)研究項目(KJ1400434)
Sparse Image Fidelity Evaluation Based on Wavelet Analysis
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摘要: 該文針對傳統(tǒng)的圖像質(zhì)量評價方法無法有效模擬人類視覺系統(tǒng)(HVS)存在的不足,提出基于小波分析的加權(quán)稀疏保真度(Weighting Sparse Fidelity, WSF)圖像評價算法。算法以模擬人類視覺系統(tǒng)的神經(jīng)網(wǎng)絡(luò)為切入點,對圖像進行一階小波分解得到4個不同方向的子帶圖像,然后將子帶圖像分成88大小的圖像塊,采用快速獨立分量分析(FastICA)的方法對各個圖像塊進行訓(xùn)練并提取圖像特征檢測矩陣,根據(jù)特征檢測矩陣計算各子帶圖像塊的稀疏特征值并建立稀疏保真度質(zhì)量評價模型。在此基礎(chǔ)上,根據(jù)細節(jié)信息的不同對低頻子帶圖像進行區(qū)間劃分并設(shè)置視覺權(quán)重,使之更加接近人眼的主觀視覺。實驗中對LIVE庫中所有圖像進行算法驗證,其結(jié)果表明,所提方法能很好地對各種失真類型的圖像進行評價。基于小波分析的稀疏保真度評價算法能夠有效模擬人類視覺系統(tǒng)的多頻特性和視覺皮層感知機制,彌補現(xiàn)有圖像質(zhì)量評價方法在此方面的不足。
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關(guān)鍵詞:
- 圖像質(zhì)量評價 /
- 稀疏保真度 /
- 獨立分量分析 /
- 視覺加權(quán) /
- 主客觀一致性
Abstract: To overcome the limitations of traditional image quality assessment methods, which are not well consistent with subjective human evaluation, a quality assessment algorithm of Weighting Sparse Fidelity (WSF) based on wavelet analysis is proposed. The arithmetic simulates nerve network of Human Vision System (HVS) as research point, the image is decomposed with wavelet into four-sub band images, which are divided into blocks at size of , then using Fast Independent Component Analysis training (FastICA) method to train the image blocks. Then, each image block sparse character matrix is extracted to calculate the sparse feature fidelity of the image and build the sparse fidelity quality evaluation model. On this basis, the image is divided into a plurality of interval according to the different details of the visual image information and a visual weight is set in each section, which can be consistent with subjective human evaluation. The experiment results on LIVE database show that the proposed method has a good evaluation of all kinds of distortion types and is highly consistent with human subjective evaluations. The proposed algorithm can effectively simulate the weighted visual cortex of the human visual system perception mechanisms, which compensates for deficiencies of existing image quality assessment methods. -
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