Abstract
This paper presents a hardware efficient pixel-domain just-noticeable difference (JND) model and its hardware architecture implemented on an FPGA. This JND model architecture is further proposed to be part of a low complexity pixel-domain perceptual image coding architecture, which is based on downsampling and predictive coding. The downsampling is performed adaptively on the input image based on regions-of-interest (ROIs) identified by measuring the downsampling distortions against the visibility thresholds given by the JND model. The coding error at any pixel location can be guaranteed to be within the corresponding JND threshold in order to obtain excellent visual quality. Experimental results show the improved accuracy of the proposed JND model in estimating visual redundancies compared with classic JND models published earlier. Compression experiments demonstrate improved rate-distortion performance and visual quality over JPEG-LS as well as reduced compressed bit rates compared with other standard codecs such as JPEG 2000 at the same peak signal-to-perceptible-noise ratio (PSPNR). FPGA synthesis results targeting a mid-range device show very moderate hardware resource requirements and over 100 Megapixel/s throughput of both the JND model and the perceptual encoder.
Highlights
Advances in sensor and display technologies have led to rapid growth in data bandwidth in high-performance imaging systems
The contrast masking and luminance masking effects are combined into the final just-noticeable difference (JND) value in the new approach, i.e., using the nonlinear additivity model for masking (NAMM) operator for texture/smooth regions and the maximum operator for edge regions
The proposed JND model and architecture are suitable for implementation on FPGAs for real-time and low complexity embedded systems
Summary
Advances in sensor and display technologies have led to rapid growth in data bandwidth in high-performance imaging systems. Compression algorithms like JPEG-LS operating in the pixel domain can be adapted to exploit the pixel-domain JND models, e.g., by setting the quantization step size adaptively based on the JND thresholds. A new region-adaptive pixel-domain JND model based on efficient local operations is proposed for a more accurate detection of visibility thresholds compared with the classic JND model [9] and for a reduced complexity compared with more recent ones [11,12]. A low complexity pixel-domain perceptual image coder [14] is used to exploit the visibility thresholds given by the proposed JND model. The coding algorithm addresses both coding efficiency and visual quality issues in conventional pixel-domain coders in a framework of adaptive downsampling guided by perceptual regions-of-interest (ROIs) based on JND thresholds.
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