SPIHT-Based Coding of the Shape and Texture of Arbitrarily Shaped Visual Objects
Bibliographic record
Abstract
A new scheme for coding both the shape and texture of arbitrarily shaped visual objects is presented. Based on set partitioning on hierarchical trees (SPIHT), the proposed Shape and Texture SPIHT (ST-SPIHT) employs a novel implementation of the shape-adaptive discrete wavelet transform (SA-DWT) using in-place lifting, along with parallel coding of texture coefficients and shape mask pixels to create a single embedded code that allows for fine-grained rate-distortion scalability. The single output code simplifies the logistics of object storage and transmission compared to previously published schemes. An input parameter provides control over the relative progression between shape and texture coding in the embedded code, allowing for adjustment of the emphasis of shape versus texture quality in low bit rate reconstructions. The combination of features provided by ST-SPIHT, namely, explicit and progressive shape coding in parallel with wavelet-based embedded coding of the object texture, is unique compared to previously published schemes. Computational complexity is minimized since the shape coding takes advantage of the decomposition and spatial orientation trees used for texture coding. Objective and subjective simulation results show that the proposed ST-SPIHT scheme has rate-distortion performance comparable or superior to MPEG-4 Visual Texture Coding for most bit rates
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".