Amitesh Kumar Singam 的个人资料, Halmstad university

India Halmstad university

个人简介: My name is Amitesh Kumar singam, i have graduated from blekinge institute of technology, sweden in the year 2011 and worked as research assistant in that university itself till 2012, later due to fina... 查看全部

Comparative Studies of Unsupervised and Supervised Learning Methods based on Multimedia Applications: 作者: -; 发表期刊: ArXiv

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Revised One, a Full Reference Video Quality Assessment Based on Statistical Based Transform Coefficient: 作者: -; 发表期刊: SSRN Electronic Journal

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A Review on Just Noticeable Difference(JND) Quality Metric towards its Drawbacks and Limitations: 作者: -; 发表期刊: SSRN Electronic Journal

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An Error Concealment based on Translations of Generalized Neural Networks: 作者: -; 发表期刊: SSRN Electronic Journal

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Classification Review of Raw Subjective Scores towards Statistical Analysis: 作者: -; 发表期刊: SSRN Electronic Journal

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Coding Estimation based on Rate Distortion Control of H.264 Encoded Videos for Low Latency Applications: 作者: -; 发表期刊: ArXiv

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Full Reference Video Quality Assessment Based on Statistical Based Transform Coefficients: 作者: Amitesh Kumar Singam, Mohammad Shahid; 发表期刊: SSRN Electronic Journal

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PROGRESSIVE STUDIES OVER SPARSENESS OF LEAST SQUARES SUPPORT VECTOR MACHINES BASED ON HYPOTHETICAL ASSUMPTION: 作者: Amitesh Kumar Singam, Bharat Reddy Ramancha, Venkat Raj Reddy Pashike, Mohammad Shahid; 发表期刊: International Journal of Scientific Research and Management

论文简介:: No Reference (NR) Video Quality Assessment is the one which is most needed in situations where the handiness of reference video is partially available which is our Hypothetical assumption due to issue raised by reviewers since we used DCT Coefficients in our past research work. Our research work explores the tradeoffs between quality prediction and Video compression. Therefore, we implemented least square support vector regression algorithm as NR-based Video Quality Metric (VQM) for quality estimation with simplified input features based on DCT coefficients (Hypothetical assumption). We concluded that our proposed model overcame sparseness due to hypothetical Assumption.

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CLASSIFICATION REVIEW OF RAW SUBJECTIVE SCORES TOWARDS STATISTICAL ANALYSIS: 作者: -; 发表期刊: International Journal of Scientific Research and Management

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Comparative Studies of Unsupervised and Supervised Learning Methods based on Multimedia Applications: 作者: -; 发表期刊: ArXiv