Just-noticeable-quantization-distortion based preprocessing for perceptual video coding

Sehwan Ki, Munchurl Kim, Hyunsuk Ko

Abstract: Conventional predictive video coding may no longer become capable of effectively accommodating the demand of high quality video services with continuously increasing spatiotemporal resolutions as before since it is reaching the limit of its coding efficiency improvement. As an alternative, perceptual video coding (PVC) is being exploited by effectively removing perceptual redundancy for coding efficiency improvement, one of which is just-noticeable-distortion (JND) directed PVC. Unfortunately, the previous JND modeling is not often suitable for JND-directed PVC approaches because quantization effects are not considered. Thus, we presents a new DCT-domain JND model that considers the quantization operation in video compression into JND modeling for PVC, which is calledjust noticeable quantization distortion (JNQD) model. Our proposed JNQD model can be applied as preprocessing prior to any video compression scheme by adding a parameter to adapt the model to quantization step sizes. For experiments, our JNQD models have been applied to High Efficiency Video Coding (HEVC) and yielded the maximum and average bitrate reductions of 37.35% and 13.05%, respectively with little subjective video quality degradation, compared to the input without preprocessing applied. Moreover, it can be applicable for any encoder as preprocessing, which can have a large flexibility compared to previous encoder-dependent schemes.