Abstract
The rate-distortion optimality of a JPEG2000 codestream is determined by the density and distribution of the quality layers it contains. The allocation of quality layers is, therefore, a fundamental issue for JPEG2000 encoders, which commonly distribute layers logarithmically or uniformly spaced in terms of bitrate, and use a rate-distortion optimization method to optimally form them. This work introduces an allocation strategy based on the hypothesis that the fractional bitplane coder of JPEG2000 already generates optimal truncation points for the overall optimization of the image. Through these overall optimal truncation points, the proposed strategy is able to allocate quality layers without employing rate-distortion optimization techniques, to self-determine the density and distribution of quality layers, and to reduce the computational load of the encoder. Experimental results suggest that the proposed method constructs near-optimal codestreams in the rate-distortion sense, achieving a similar coding performance as compared with the common PCRD-based approach.
Highlights
JPEG2000 is a powerful standard structured in 12 parts that addresses the coding, transmission, security, and manipulation of still images and video
To assess the Tier-1’s computational load reduction achieved by SCALE, we have encoded all images of the corpus to the target bitrates reported in Table 2, computing the time spent by SCALE and the Post-Compression Rate-Distortion (PCRD) method when encoding at those bitrates
Compared to the results reported in the literature, there are only two rate-distortion optimization methods [13, 20] able to achieve speed-ups similar to the reported ones, suggesting that SCALE is highly competitive in terms of the computational load reduction of the Tier-1 stage
Summary
JPEG2000 is a powerful standard structured in 12 parts that addresses the coding, transmission, security, and manipulation of still images and video. The final codestream is formed through ratedistortion optimization techniques that optimally truncate these bitstreams, and through the Tier-2 stage that encodes the auxiliary information needed to properly decode the image. In this coding process, rate-distortion optimization is necessary for two main reasons [3]: (1) to attain a target bitrate for the final codestream while minimizing the overall image distortion; (2) to form increasing layers of quality that avoid penalizing the quality of the decoded image when the codestream is truncated, or the image is interactively transmitted
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