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https://github.com/curioustorvald/tsvm.git
synced 2026-03-08 20:21:50 +09:00
still working on the psychovisual model
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minjaesong
@@ -11,7 +11,65 @@ import net.torvald.terrarum.modulecomputers.virtualcomputer.tvd.toUint
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import net.torvald.tsvm.peripheral.GraphicsAdapter
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import net.torvald.tsvm.peripheral.PeriBase
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import net.torvald.tsvm.peripheral.fmod
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import java.util.*
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import kotlin.Any
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import kotlin.Array
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import kotlin.Boolean
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import kotlin.BooleanArray
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import kotlin.Byte
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import kotlin.ByteArray
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import kotlin.Double
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import kotlin.Exception
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import kotlin.Float
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import kotlin.FloatArray
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import kotlin.IllegalArgumentException
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import kotlin.IllegalStateException
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import kotlin.Int
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import kotlin.IntArray
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import kotlin.Long
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import kotlin.LongArray
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import kotlin.Pair
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import kotlin.Short
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import kotlin.ShortArray
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import kotlin.String
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import kotlin.Triple
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import kotlin.arrayOf
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import kotlin.byteArrayOf
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import kotlin.collections.ArrayList
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import kotlin.collections.List
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import kotlin.collections.MutableMap
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import kotlin.collections.component1
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import kotlin.collections.component2
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import kotlin.collections.component3
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import kotlin.collections.component4
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import kotlin.collections.copyOf
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import kotlin.collections.count
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import kotlin.collections.fill
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import kotlin.collections.forEach
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import kotlin.collections.forEachIndexed
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import kotlin.collections.indices
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import kotlin.collections.isNotEmpty
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import kotlin.collections.listOf
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import kotlin.collections.map
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import kotlin.collections.maxOfOrNull
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import kotlin.collections.mutableListOf
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import kotlin.collections.mutableMapOf
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import kotlin.collections.set
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import kotlin.collections.sliceArray
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import kotlin.collections.sorted
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import kotlin.collections.sumOf
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import kotlin.collections.toFloatArray
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import kotlin.error
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import kotlin.floatArrayOf
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import kotlin.fromBits
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import kotlin.intArrayOf
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import kotlin.let
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import kotlin.longArrayOf
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import kotlin.math.*
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import kotlin.repeat
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import kotlin.text.format
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import kotlin.text.lowercase
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import kotlin.text.toString
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class GraphicsJSR223Delegate(private val vm: VM) {
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@@ -3888,7 +3946,7 @@ class GraphicsJSR223Delegate(private val vm: VM) {
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return subbands
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}
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private fun getPerceptualWeight(level: Int, subbandType: Int, isChroma: Boolean, maxLevels: Int): Float {
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private fun getPerceptualWeightModel2(level: Int, subbandType: Int, isChroma: Boolean, maxLevels: Int): Float {
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// Psychovisual model based on DWT coefficient statistics and Human Visual System sensitivity
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if (!isChroma) {
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@@ -4031,6 +4089,116 @@ class GraphicsJSR223Delegate(private val vm: VM) {
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}*/
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}
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var ANISOTROPY_MULT = floatArrayOf(1.8f, 1.6f, 1.4f, 1.2f, 1.0f, 1.0f)
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var ANISOTROPY_BIAS = floatArrayOf(0.2f, 0.1f, 0.0f, 0.0f, 0.0f, 0.0f)
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private fun perceptual_model3_LH(quality: Int, level: Int): Float {
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val H4 = 1.2f
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val Lx = H4 - ((quality + 1f) / 15f) * (level - 4f)
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val Ld = (quality + 1f) / -15f
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val C = H4 - 4f * Ld - ((-16f * (quality - 5f)) / (15f))
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val Gx = (Ld * level) - (((quality - 5f) * (level - 8f) * level) / (15f)) + C
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return if (level >= 4) Lx else Gx
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}
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private fun perceptual_model3_HL(quality: Int, LH: Float): Float {
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return LH * ANISOTROPY_MULT[quality] + ANISOTROPY_BIAS[quality]
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}
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private fun perceptual_model3_HH(LH: Float, HL: Float): Float {
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return 2f * (LH + HL) / 3f
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}
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fun perceptual_model3_LL(quality: Int, level: Int): Float {
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val n = perceptual_model3_LH(quality, level)
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val m = perceptual_model3_LH(quality, level - 1) / n
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return n / m
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}
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private val FOUR_PIXEL_DETAILER = 0.88f
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private fun getPerceptualWeight(qYGlobal: Int, level: Int, subbandType: Int, isChroma: Boolean, maxLevels: Int): Float {
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// Psychovisual model based on DWT coefficient statistics and Human Visual System sensitivity
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val qualityLevel = if (qYGlobal >= 60) 0
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else if (qYGlobal >= 42) 1
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else if (qYGlobal >= 25) 2
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else if (qYGlobal >= 12) 3
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else if (qYGlobal >= 6) 4
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else if (qYGlobal >= 2) 5
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else 5
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if (!isChroma) {
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// LUMA CHANNEL: Based on statistical analysis from real video content
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// LL subband - contains most image energy, preserve carefully
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if (subbandType == 0) return perceptual_model3_LL(qualityLevel, level)
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// LH subband - horizontal details (human eyes more sensitive)
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val LH: Float = perceptual_model3_LH(qualityLevel, level)
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if (subbandType == 1) return LH
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// HL subband - vertical details
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val HL: Float = perceptual_model3_HL(qualityLevel, LH)
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if (subbandType == 2) return HL * (if (level == 3) FOUR_PIXEL_DETAILER else 1f)
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// HH subband - diagonal details
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else return perceptual_model3_HH(LH, HL) * (if (level == 3) FOUR_PIXEL_DETAILER else 1f)
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} else {
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// CHROMA CHANNELS: Less critical for human perception, more aggressive quantization
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when (subbandType) {
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0 -> { // LL chroma - still important but less than luma
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return 1f
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return when {
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level >= 6 -> 0.8f // Chroma LL6: Less critical than luma LL
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level >= 5 -> 0.9f
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else -> 1.0f
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}
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}
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1 -> { // LH chroma - horizontal chroma details
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return 1.8f
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return when {
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level >= 6 -> 1.0f
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level >= 5 -> 1.2f
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level >= 4 -> 1.4f
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level >= 3 -> 1.6f
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level >= 2 -> 1.8f
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else -> 2.0f
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}
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}
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2 -> { // HL chroma - vertical chroma details (even less critical)
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return 1.3f;
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return when {
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level >= 6 -> 1.2f
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level >= 5 -> 1.4f
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level >= 4 -> 1.6f
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level >= 3 -> 1.8f
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level >= 2 -> 2.0f
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else -> 2.2f
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}
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}
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3 -> { // HH chroma - diagonal chroma details (most aggressive)
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return 2.5f
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return when {
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level >= 6 -> 1.4f
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level >= 5 -> 1.6f
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level >= 4 -> 1.8f
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level >= 3 -> 2.1f
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level >= 2 -> 2.3f
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else -> 2.5f
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}
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}
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}
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}
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return 1.0f
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}
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// Helper function to calculate five-number summary for coefficient analysis
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private fun calculateFiveNumberSummary(values: List<Int>): String {
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if (values.isEmpty()) return "empty"
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@@ -4046,20 +4214,18 @@ class GraphicsJSR223Delegate(private val vm: VM) {
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return "min=$min, Q1=$q1, med=%.1f, Q3=$q3, max=$max, n=$n".format(median)
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}
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private fun dequantiseDWTSubbandsPerceptual(quantised: ShortArray, dequantised: FloatArray,
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private fun dequantiseDWTSubbandsPerceptual(qYGlobal: Int, quantised: ShortArray, dequantised: FloatArray,
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subbands: List<DWTSubbandInfo>, baseQuantizer: Float, isChroma: Boolean, decompLevels: Int) {
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// Initialize output array to zero (critical for detecting missing coefficients)
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for (i in dequantised.indices) {
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dequantised[i] = 0.0f
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}
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Arrays.fill(dequantised, 0.0f)
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// Track coefficient coverage for debugging
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var totalProcessed = 0
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var maxIdx = -1
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for (subband in subbands) {
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val weight = getPerceptualWeight(subband.level, subband.subbandType, isChroma, decompLevels)
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val weight = getPerceptualWeight(qYGlobal, subband.level, subband.subbandType, isChroma, decompLevels)
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// CRITICAL FIX: Use the same effective quantizer as encoder for proper reconstruction
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val effectiveQuantizer = baseQuantizer * weight
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@@ -4129,7 +4295,7 @@ class GraphicsJSR223Delegate(private val vm: VM) {
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try {
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// Determine if monoblock mode based on TAV version
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val isMonoblock = (tavVersion == 3 || tavVersion == 4 || tavVersion == 5 || tavVersion == 6)
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val isMonoblock = (tavVersion >= 3)
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val tilesX: Int
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val tilesY: Int
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@@ -4168,13 +4334,13 @@ class GraphicsJSR223Delegate(private val vm: VM) {
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}
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0x01 -> { // TAV_MODE_INTRA
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// Decode DWT coefficients directly to RGB buffer
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readPtr = tavDecodeDWTIntraTileRGB(readPtr, tileX, tileY, currentRGBAddr,
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readPtr = tavDecodeDWTIntraTileRGB(qYGlobal, readPtr, tileX, tileY, currentRGBAddr,
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width, height, qY, qCo, qCg,
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waveletFilter, decompLevels, isLossless, tavVersion, isMonoblock)
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}
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0x02 -> { // TAV_MODE_DELTA
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// Coefficient delta encoding for efficient P-frames
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readPtr = tavDecodeDeltaTileRGB(readPtr, tileX, tileY, currentRGBAddr,
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readPtr = tavDecodeDeltaTileRGB(qYGlobal, readPtr, tileX, tileY, currentRGBAddr,
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width, height, qY, qCo, qCg,
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waveletFilter, decompLevels, isLossless, tavVersion, isMonoblock)
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}
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@@ -4187,7 +4353,7 @@ class GraphicsJSR223Delegate(private val vm: VM) {
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}
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}
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private fun tavDecodeDWTIntraTileRGB(readPtr: Long, tileX: Int, tileY: Int, currentRGBAddr: Long,
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private fun tavDecodeDWTIntraTileRGB(qYGlobal: Int, readPtr: Long, tileX: Int, tileY: Int, currentRGBAddr: Long,
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width: Int, height: Int, qY: Int, qCo: Int, qCg: Int,
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waveletFilter: Int, decompLevels: Int, isLossless: Boolean, tavVersion: Int, isMonoblock: Boolean = false): Long {
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// Determine coefficient count based on mode
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@@ -4247,9 +4413,9 @@ class GraphicsJSR223Delegate(private val vm: VM) {
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val tileHeight = if (isMonoblock) height else PADDED_TILE_SIZE_Y
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val subbands = calculateSubbandLayout(tileWidth, tileHeight, decompLevels)
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dequantiseDWTSubbandsPerceptual(quantisedY, yTile, subbands, qY.toFloat(), false, decompLevels)
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dequantiseDWTSubbandsPerceptual(quantisedCo, coTile, subbands, qCo.toFloat(), true, decompLevels)
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dequantiseDWTSubbandsPerceptual(quantisedCg, cgTile, subbands, qCg.toFloat(), true, decompLevels)
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dequantiseDWTSubbandsPerceptual(qYGlobal, quantisedY, yTile, subbands, qY.toFloat(), false, decompLevels)
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dequantiseDWTSubbandsPerceptual(qYGlobal, quantisedCo, coTile, subbands, qCo.toFloat(), true, decompLevels)
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dequantiseDWTSubbandsPerceptual(qYGlobal, quantisedCg, cgTile, subbands, qCg.toFloat(), true, decompLevels)
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// Debug: Check coefficient values before inverse DWT
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if (tavDebugCurrentFrameNumber == tavDebugFrameTarget) {
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@@ -4777,7 +4943,7 @@ class GraphicsJSR223Delegate(private val vm: VM) {
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}
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}
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private fun tavDecodeDeltaTileRGB(readPtr: Long, tileX: Int, tileY: Int, currentRGBAddr: Long,
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private fun tavDecodeDeltaTileRGB(qYGlobal: Int, readPtr: Long, tileX: Int, tileY: Int, currentRGBAddr: Long,
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width: Int, height: Int, qY: Int, qCo: Int, qCg: Int,
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waveletFilter: Int, decompLevels: Int, isLossless: Boolean, tavVersion: Int, isMonoblock: Boolean = false): Long {
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@@ -4849,9 +5015,9 @@ class GraphicsJSR223Delegate(private val vm: VM) {
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val deltaCoFloat = FloatArray(coeffCount)
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val deltaCgFloat = FloatArray(coeffCount)
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dequantiseDWTSubbandsPerceptual(deltaY, deltaYFloat, subbands, qY.toFloat(), false, decompLevels)
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dequantiseDWTSubbandsPerceptual(deltaCo, deltaCoFloat, subbands, adjustedQCo, true, decompLevels)
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dequantiseDWTSubbandsPerceptual(deltaCg, deltaCgFloat, subbands, adjustedQCg, true, decompLevels)
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dequantiseDWTSubbandsPerceptual(qYGlobal, deltaY, deltaYFloat, subbands, qY.toFloat(), false, decompLevels)
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dequantiseDWTSubbandsPerceptual(qYGlobal, deltaCo, deltaCoFloat, subbands, adjustedQCo, true, decompLevels)
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dequantiseDWTSubbandsPerceptual(qYGlobal, deltaCg, deltaCgFloat, subbands, adjustedQCg, true, decompLevels)
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// Reconstruct: current = previous + perceptually_dequantized_delta
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for (i in 0 until coeffCount) {
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