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DPICT is the first learning-based image codec supporting fine granularscalability. In this paper, we describe how to implement two key components ofDPICT efficiently: trit-plane slicing and rate-distortion-optimized(RD-optimized) coding. In DPICT, we transform an image into a latent tensor,represent the tensor in ternary digits (trits), and encode the trits in thedecreasing order of significance. For entropy encoding, it is necessary tocompute the probability of each trit, which demands high time complexity inboth the encoder and the decoder. To reduce the complexity, we develop aparallel computing scheme for the probabilities, which is described in detailwith pseudo-codes. Moreover, we compare the trit-plane slicing in DPICT withthe alternative bit-plane slicing. Experimental results show that the timecomplexity is reduced significantly by the parallel computing and that thetrit-plane slicing provides better RD performances than the bit-plane slicing.

RD-Optimized Trit-Plane Coding of Deep Compressed Image Latent Tensors