![]() It usually occurs in regions that have texture, like trees, fields of grass, waves, etc. However, whereas the ringing effect is restricted to sharp edges or lines, the basis pattern is not. ![]() The artifact appears similar to the ringing effect. The basis pattern effect takes its name from basis functions (mathematical transforms) endemic to all compression algorithms. Objective evaluation of temporal artifacts is more challenging, though, and popular VQA models often fail to account for them. Therefore interframe algorithms typically show improved video compression rates, but at the expense of propagating compression losses to subsequent frame predictions – this propagation and “rounding on rounding” is the origin of many temporal artifacts. I-frame-based algorithms like MJPEG are less susceptible to temporal artifacts since I-frames are single image encodings, while P‑frames and B‑frames hold only part of the image information. The compression algorithm being used will either utilize the I-frame (intraframe) or P- B-frames (interframe). If it’s much more visible while the video plays, then it’s likely temporal. If you can see the artifact when the video is paused, then it’s probably a spatial artifact. In B2B settings, poor-quality video often results in trouble tickets with highly actionable comments like “the video’s bad,” “choppy,” or “laggy.” There are several types of compression artifacts that can be the cause of a “bad” video, though, so it may be useful for troubleshooting to be able to identify the different artifacts and recognize when and where you’re most likely to encounter them.Īrtifacts are first categorized by whether they’re time/sequence-based (temporal) or location-based (spatial). The same study indicated that roughly 60% of all video streams experienced quality degradation.Īlthough streaming technology has made significant advancements in quality during the last 5 years (albeit somewhat offset by the increased demand in bandwidth), the fact remains that poor video quality continues to challenge viewers’ patience and is a significant hurdle for video content vendors. ![]() One study* indicated that in 2012, global content brands lost $2.16 billion of revenue due to poor quality video streams, and were expected to lose up to $20 billion through 2017 as a result of quality issues. This can have a significant negative impact on revenue for content providers. Consumers are already used to the high compression ratios (and any accompanying artifacts) necessary for delivering video over wireless and mobile (H.264/AVC or H.265/HEVC), but there’s still a tipping point at which the consumer stops watching if the video quality is too poor.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |