Abstract
The Structural Similarity (SSIM) Index is a very widely used image/video quality model that continues to play an important role in the perceptual evaluation of compression algorithms, encoding recipes and numerous other image/video processing algorithms. Several public implementations of the SSIM and Multiscale-SSIM (MS-SSIM) algorithms have been developed, which differ in efficiency and performance. This "bendable ruler" makes the process of quality assessment of encoding algorithms unreliable. To address this situation, we studied and compared the functions and performances of popular and widely used implementations of SSIM, and we also considered a variety of design choices. Based on our studies and experiments, we have arrived at a collection of recommendations on how to use SSIM most effectively, including ways to reduce its computational burden.
Highlights
With the explosion of social media platforms and online streaming services, video has become the most widely consumed form of content on the internet, accounting for 60% of global internet traffic in 2019 [1]
After linearizing the Structural Similarity (SSIM) values in this manner, we report the Pearson Correlation Coefficient (PCC) which is a measure of the linear correlation between the predicted and true quality, the Spearman Rank Order Correlation Coefficient (SROCC) which is a measure of the rank correlation, and the Root Mean Square Error (RMSE) which is a measure of the error in predicting subjective quality
The columns represent the choice of spatial pooling (SP) method, while the rows represent the choice of temporal pooling (TP) methods
Summary
With the explosion of social media platforms and online streaming services, video has become the most widely consumed form of content on the internet, accounting for 60% of global internet traffic in 2019 [1]. Social media platforms have led to an explosion in the amount of image data being shared and stored online Handling such large volumes of image and video data is inconceivable without the use of compression algorithms such as JPEG [2], [3], AVIF (AV1 Intra) [4], [5], HEIF (HEVC Intra) [6], H.264 [7], [8], HEVC [9], EVC [10], VP9 [11], AV1 [12], SVT-AV1 [13], and the upcoming VVC and AV2 standards. These weighting functions sum to unity, and have a finite-extent Gaussian or rectangular shape.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
