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An HEVC-Compliant Perceptual Video Coding Scheme Based on JND Models for Variable Block-Sized Transform Kernels

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PROJECT TITLE :

An HEVC-Compliant Perceptual Video Coding Scheme Based on JND Models for Variable Block-Sized Transform Kernels

ABSTRACT:

In this paper, a High Potency Video Coding (HEVC)-compliant perceptual video coding (PVC) theme is introduced based mostly on just-noticeable distinction (JND) models in both transform and pixel domains. We adopt an existing pixel-domain JND model for the remodel skip mode of HEVC and propose a transform-domain JND model for the remodel nonskip modes of HEVC. The proposed remodel-domain JND model is intended by considering the spatial JND characteristics like contrast sensitivity, luminance adaptation, and distinction masking effects in addition to by considering the summation effects of variable block-sized transforms in HEVC. A temporal JND model is additionally incorporated into the proposed transform-domain JND model to additional reduce perceptual redundancy. To include the transform- and pixel-domain JND models into the encoding method in an HEVC-compliant manner, the remodel coefficients and residues are suppressed in harmonization with the rework/quantization process and therefore the quantization-solely process of HEVC, respectively. To form the JND-based suppression effective, a distortion compensation factor is also proposed to reflect the perceptual distortion in the rate-distortion optimization-based mostly encoding process. Based mostly on subjective quality assessments of the encoded bit streams of test sequences, the proposed HEVC-compliant PVC theme yields remarkable bitrate reductions of a maximum 49.ten% and an average sixteen.tenp.c with negligible subjective quality loss, compared with an HEVC reference software HEVC check model (HM eleven.0). Further, the proposed HEVC-compliant PVC scheme increases the encoding complexity of HM eleven.0 only by a median of eleven.25p.c.


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An HEVC-Compliant Perceptual Video Coding Scheme Based on JND Models for Variable Block-Sized Transform Kernels - 4.9 out of 5 based on 15 votes

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