Download PDFOpen PDF in browser

Computational Complexity of Human Visual Characteristics Based on Well Known Metric

EasyChair Preprint no. 9481

3 pagesDate: December 15, 2022


We proposed a well-known model for quality estimation based on compression artifacts which are principal factor for determining the Quality of Experience (QoE). Moreover, contents with strong compression have been rejected by viewers and one who graded quality of distorted videos have judged unacceptable in my past research work. It happened due to User Experience because most of viewers were highly experienced one and are also related to same field in the same university. The results of our proposed approach confirm that our subjective scores of 120 videos are related to control of visible spatial artifacts of reconstructed videos not distorted which means it was coded within basic compression and moreover our research interest lies within validation of Subjective and objective quality assessment. We conclude that Subjective scores are considered as independent variables and input features of H.264 bitstream data as dependent variable and moreover input features are validated by correlation coefficients.

Keyphrases: H.264, MLR, QoE, VQM

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Amitesh Kumar Singam and Venkat Raj Reddy Pashike},
  title = {Computational Complexity of Human Visual Characteristics Based on Well Known Metric},
  howpublished = {EasyChair Preprint no. 9481},

  year = {EasyChair, 2022}}
Download PDFOpen PDF in browser